Reviewerly/exHarmony huggingface dataset:
Non - Linear Analysis with Experiment of Rc Tower with wind Loading Telecommunication

The purpose for this article is to apprehend the application of Non linear analysis related to the wind loading telecommunication. It has been noted that the Physical deficiency was explained by the concept of stiffness and the P-delta technique was used to calculate the absence of a geometric line. To show the hardness performance, we use a volume equation. In each exchange of both P-Delta channels, we calculate the NDA by stabilizing it according to the performance of the power level. The frequency response and vibration structure were combined using the hard work obtained in the final stage of the P-Delta approach. We think the structure has been changed to change the balance of the P-Delta route. Finally, the combination of non-linear forces was assessed statistically and dynamically. A topology improvement design technique is used to set up a new communication tower network. The topology improvement technique results in significant reductions in total dissociation systems, as well as total weight reduction. The number of connections and the need to maintain connections and ownership are reduced. As a result of improved exoskeleton architecture, TDC performance has improved between the two axes. The destination of the device is clearly visible due to increased stiffness
A Comprehensive Analysis in Investigating the Impact of Big Data Analytics in Wireless Networks in Failure Prediction, Prevention and Recovery

The current economic and competitive environment has enabled many businesses and industries to implement digital for enhancing the business operations and support in addressing the overall objectives. Dur to this there is a mounting avalanche information being shared between the individuals, companies and governments, the increase in the usage of mobile devices, internet, social media and other ecommerce services has supported in increasing the volume of data. This trend is expected to grow exponentially and hence there is a requirement to provide wireless network in an effective manner, the increased usage of data and information has resulted in facing various failures, like call drops for mobile users, low bandwidth for internet users etc. Hence, it is highly necessary to implement tools to understand the failure prediction, prevention and recovery on the network. Hence, this study is focused in investigating the impact of big data analysis in protecting the wireless networks from such failures and support in processing large volume of information in an effective manner, the big data analytics can also extract useful data will enable in addressing the concerned parties so as to make critical decisions. In this study, the researcher intends to conduct the survey using closed ended questionnaire from the individuals so as to measure the effectiveness of applying big data for failure prevention, protection and recovery in the wireless network.
Target Detection and Multi-Feature Fusion Based on Deep Learning and CamShift Algorithm

Aiming at the problem that the continuous adaptive mean shift algorithm (Camshift) deep learning tracking algorithm based on the colour probability distribution can easily cause the tracking target to fail when the same colour interference appears in the background, we propose an improved Camshift tracking algorithm. The article first conducts target detection before Camshift tracks the target, and compares three detection algorithms through frame difference method, optical flow method, and background difference method. Then, when tracking the target, the window centre of the CamShift algorithm is compared with the centre of the target area calculated by the difference method to determine the subsequent frame search window to avoid the loss of target tracking; finally, the experiment proves that the method can effectively track the target, and it still has a very high accuracy rate when the target colour and the background colour are small.
Summary of Workshops and Tutorials at European Semantic Web Conference 2022

This document summarizes the workshops and tutorials of the 19th European Semantic Web Conference. This edition accepted 10 workshops on different topics revolving around knowledge graphs, such as natural language processing, industrial use of knowledge graphs, biomedical data, etc. Moreover, 2 tutorials were accepted which included knowledge graph construction and music encodings.
KnowReQA: A Knowledge-aware Retrieval Question Answering System

Retrieval question answering (ReQA) is an essential mechanism to automatically satisfy the users’ information needs and overcome the problem of information overload. As a promising solution to achieve fast retrieval from large-scale candidate answers, dual-encoder framework has been widely studied to improve its representation quality for text in the recent years. Inspired by that humans usually answer the question using their background knowledge, in this work, we explore the way to incorporate knowledge entities into the retrieval model to build high-quality text representations and propose novel knowledge-aware text encoding and knowledge-aware text matching modules to facilitate the fusion between text and knowledge. The promising experimental results on various benchmarks prove the potential of the proposed approach.
Pruned Graph Neural Network for Short Story Ordering

Text coherence is a fundamental problem in natural language generation and understanding. Organizing sentences into an order that maximizes coherence is known as sentence ordering. This paper is proposing a new approach based on the graph neural network approach to encode a set of sentences and learn orderings of short stories. We propose a new method for constructing sentence-entity graphs of short stories to create the edges between sentences and reduce noise in our graph by replacing the pronouns with their referring entities. We improve the sentence ordering by introducing an aggregation method based on majority voting of state-of-the-art methods and our proposed one. Our approach employs a BERT-based model to learn semantic representations of the sentences. The results demonstrate that the proposed method significantly outperforms existing baselines on a corpus of short stories with a new state-of-the-art performance in terms of Perfect Match Ratio (PMR) and Kendall’s Tau ( $$\tau $$ ) metrics. More precisely, our method increases PMR and $$\tau $$ criteria by more than 5% and 4.3%, respectively. These outcomes highlight the benefit of forming the edges between sentences based on their cosine similarity. We also observe that replacing pronouns with their referring entities effectively encodes sentences in sentence-entity graphs.
It’s All in the Name: Entity Typing Using Multilingual Language Models

The entity type information in Knowledge Graphs (KGs) of different languages plays an important role in a wide range of Natural Language Processing applications. However, the entity types in KGs are often incomplete. Multilingual entity typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, multilingual neural language models are exploited to predict the type of an entity from only the name of the entity. The model has been successfully evaluated on multilingual datasets extracted from different language chapters in DBpedia namely German, French, Spanish, and Dutch.
IMPROVING ENGLISH SPEAKING SKILLS TROUGH YOUTUBE

The purpose of this study was to find out if the ability to speak English effectively by 8th grades at MTs Babussalam was improved by using YouTube. This paper focused on implementing YouTube as method for teaching speaking that is different, interesting, and fun, while creating a classroom atmosphere that is comfortable and increases, students’ enthusiasm for learning. This paper used the qualitative descriptive method as the method of research. The result of using YouTube was very effective because it followed students to speak with more confidence and expression. They also did not have to worry about the phrases they used when speaking because they were able to see how foreigners speak English properly and correctly
Robotic Online Competition: Experience and Resources

The article presents the experience of preparing and conducting an online robotic competition in a virtual environment. This approach allows you to cover remote participants in the competition, including those in different countries, based on the use of a robot model control simulator based on programming. Competitors get the opportunity to creatively apply programming skills in an applied robotic environment for various models of virtual robots and their control areas.
Comparative analysis of binary classifiers on an array of scientific publications

Binary classifiers are studies on balanced text samples. The samplings are formed from scientific publications in the field of Computer Science (Computer Science). The first class contains articles on «Text Data Mining» (the «TDM» class), the second one contains works on other topics of Computer Science (the «non-TDM» class). All the main stages of preliminary processing of text documents are considered, models of their presentation are analyzed. The problem of binary classification is formulated and the quality indicators used in the study are given. A method of sampling from the Russian digital library (Elibrary) is proposed. The generated sampling consists of bibliographic descriptions of documents (title, abstract and keywords). An exploratory analysis was carried out and the sampling structure was studied. «Term clouds» for two classes are constructed and analyzed, documents are visualized using the method of stochastic embedding of neighbors with t -distribution ( t -SNE). Based on the review and analysis of known classifiers, the following methods were selected for the study: the K -nearest neighbor method, random forest, gradient boosting, logistic regression, and the support vector method. Profile methods based on the construction of a vector (profile) of the most informative terms determined by the frequency of occurrence of terms and classes are also used in the study. The parameters of the methods were configured using a five-fold cross-validation. The best quality of classification in our sampling demonstrated the methods using the ensemble (collective) decision-making principle (random forest, gradient boosting), as well as the support vector method. The best classifier, gradient boosting, had the proportion of correct answers (accuracy) about 0.98, recall and precision about 0.99. The other (simpler) methods used in the study also generally showed rather good quality of classification (for the least accurate k-nearest neighbor method accuracy, recall and precision were 0.90, 0.81, and 0.91, respectively).
DESIGN EDUCATION IN BUSINESS INNOVATION

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 9894-9900ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.2383Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Exploratory Study on Digital Policy and Trust in Parliament in Digital Transformation Era

In the wake of digital transformation, Parliament should implement digital policies to ensure trust. However, digital technology by itself is unlikely to produce meaningful results in the policy-making process. Accordingly, this study focuses on the coexistence of high-level digital policy governance and distrust in Parliament. Furthermore, it explores the necessary conditions of digital policy for promoting public confidence in Parliament in this era of digital transformation. In particular, by analyzing the level of institution-based and process-based digital policies, this study examines the implications of the relationship between digital policy and trust in Parliament. Finally, to ensure trust in Parliament and reestablish its roles and functions, it is imperative to improve process-based digital policies.
An empirical study on the relationship between frequency of request for FTA verification of origin and internal factors of exporting firms

The increase of the FTA concluded increased the share of trade, but also increased the demand for verification of origin. The verification of origin is conducted for the purpose of prevention of bypass import of third domestic products and prevention of customs tariffs on exported goods. The purpose of this study is to investigate the relationship between the FTA support of the management team, the export experience of the professional manpower, the operation and education of the departments of origin, and the frequency of requesting the verification of origin. A total of 384 questionnaires were used for empirical analysis. The experience of exporting the skilled workforce and the operation and education of the departments responsible for the origin are determined to have a negative impact on the frequency of request for origin verification, and both hypotheses were adopted. However, the FTA support of the management and the ability to prepare for origin verification did not affect the frequency of request for country of origin verification, and both hypotheses were rejected.
Obituary

Journal of Analytical PsychologyVolume 67, Issue 3 p. 909-910 Obituary Obituary Hester McFarland Solomon February 11th, 1943 - October 5th, 2021 First published: 20 July 2022 https://doi.org/10.1111/1468-5922.12824Read 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 onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume67, Issue3June 2022Pages 909-910 RelatedInformation
Case Exploration of Access Database Application Course Based on Bloom’s Taxonomy

With the transformation and upgrading of the new generation of information technology and the introduction of a series of policies of "new engineering", the cultivation of innovative technical talents and the construction of first-class courses are inevitable. The curriculum is the concretization, operationalization, and objectification of the fundamental standard of talent training "Building Morality and cultivating people". In this context, based on bloom classification, we explore how to effectively design cases in the course construction of access database application, improve the teaching effect and students' basic skills and abilities, and be ready for future study and work.
Risk Management

Most of the things that go wrong with information system development projects have absolutely nothing to do with computers.
Autonomous Navigation System for Indoor Mobile Robots Based on a Multi-sensor Fusion Technology

Map construction and path planning are two critical problems for an autonomous navigation system. One traditional map construction method is to construct a 2D grid map based on LiDAR, but this method has some limits. It easily ignores 3D information which affects the accuracy of navigation. Another one is visual SLAM techniques, such as ORB-SLAM2 and S-PTAM algorithms, which can recognize 3D objects. But the visual methods perform not well because of light changes. Some conventional path planning algorithms, such as TEB and DWA, are proposed for auto-navigation. However, those algorithms are likely to go to a stalemate due to local optimum, or have the problems of collision caused by sudden speed changes in constrained environments. In order to address these issues, this paper proposes a multi-sensor fusion method for map construction and autonomous navigation. Firstly, the fusion model combines RGB-D, lidar laser, and inertial measurement unit (IMU) to construct 2D grid maps and 3D color point cloud maps in real-time. Next, we present an improved local planning algorithm (Opt_TEB) to solve the velocity mutation problem, enabling the robot to get a collision-free path. We implemented the whole system based on the ROS framework, which is a wide used an open-source robot operating system. The map construction and path planning algorithms are running on the robot, while the visualization and control modules are deployed on a back-end server. The experimental results illustrate that the multi-sensor fusion algorithm is able to conform to the original map more than the 2D grid map. Furthermore, our improved algorithm Opt_TEB performs smoothly and has no collision with obstacles in 30 trials. The navigation speed is improved by 4.2% and 11.5% compared to TEB and DWA, respectively.
Two-Stage Clustering for Federated Learning with Pseudo Mini-batch SGD Training on Non-IID Data

Statistical heterogeneity problem in federated learning is mainly caused by the skewness of the data distribution among clients. In this paper, we first discover a connection between the discrepancy of data distributions and their model divergence. Based on this insight, we introduce a K-center clustering method to build client groups by the similarity of their local updating parameters, which can effectively reduce the data distribution skewness. Secondly, this paper provides a theoretical proof that a more uniform data distribution of clients in training can reduce the growth of model divergence thereby improving the training performance on Non-IID environment. Therefore, we randomly divide the clients of each cluster in the first stage into multiple fine-grained clusters to flatten the original data distribution. Finally, to fully leverage the data in each fine-grained cluster for training, we proposed an intra-cluster training method named pseudo mini-batch SGD training. This method can conduct general mini-batch SGD training on each fine-grained cluster with data kept locally. With the two-stage clustering mechanism, the negative effect of Non-IID data can be steadily eliminated. Experiments on two federated learning benchmarks i.e. FEMNIST and CelebA, as well as a manually setting Non-IID dataset using CIFAR10 show that our proposed method significantly improves training efficiency on Non-IID data and outperforms several widely-used federated baselines.
Face Detection and Recognization System

Face recognition taken image or video could be a common topic in biometry analysis several public places typically have police investigation cameras for video capture associate in nursingd these cameras have their important worth for security purpose it's wide acknowledged that the face recognition have contend a crucial role in closed-circuit television because it doesnt want the objects cooperation the particular blessings of face based mostly} identification over different biometry area unit individualism and acceptance as face could be a dynamic object with high degree of variability in its look that creates face detection a troublesome in laptop vision in accuracy and speed of identification could be a main issue the goal of this paper is to guage numerous face detection and recognition strategies give complete answer for image based face detection and recognition with higher accuracy higher response rate as an initial step for video police investigation answer is projected supported performed tests on numerous face to richs databases in terms of subjects create emotions race and light-weight.
Uncertain Graph Publishing of Social Networks for Objective Weighting of Nodes

With the rapid rise of social network platforms, more users share their daily lives through social networks, which has led to a large amount of personal privacy being leaked or stolen. Therefore, secure social network data publishing has become the focus of research in the field of data publishing. Aiming at the problem of low data availability caused by node weights that are not considered in the existing privacy protection methods for publishing uncertain graphs, this paper proposes a privacy protection method for publishing uncertain graphs based on objective weighting. In this paper, the entropy weight method is used to objectively weight the nodes, and then the Laplacian mechanism is used to add noise to the edges and convert the noise into the probability of edges, and finally generate and publish an uncertain graph. Experiments have proved that the method proposed in this paper can effectively improve the usability of social network structure while ensuring privacy.
Aspects Relating the Influence of Spirituality on CSR in Online Organizations

In the last years, the emphasis on the concept of "Corporate Social Responsibility", abbreviated as (CSR) is increasing due to the influence of the digital era and competition.The access to information is instantaneously, thus, it is difficult for companies to maintain a positive identity without involving themselves in social causes.Spirituality could lead to a level of awareness that could direct towards more social responsibility and strong relationships.Spiritual individuals are responsible human beings, who take into consideration their actions in general.Therefore, it could be a powerful correlation between CSR and spirituality.This article analyzes the influence of spirituality on CSR in organizations that operate also online.The scope of this research is to see whether there are interdependencies between CSR and spiritual responsible behaviour.Does spiritual awareness or spirituality could change the approach regarding corporate social responsibility, and respectively be included in CSR norms.To verify these aspects, two questionnaires have been applied to a sample of 217 people who are employees and entrepreneurs.All have responded from proper initiative.The number of questions was 9 in each questionnaire.As well, the same questions were asked, but adapted to each category.The questionnaires have been used to establish several indicators related to the social corporate responsibility.This research is useful to all parties involved in CSR.
Professional Training in Higher Education: Technological Aspect

The development of science and technology offers new forms of communication, new types of resolution of abstract and specific tasks, turning the teacher from an authoritarian translator of ready-made ideas into an inspirer of intellectual and creative potential of students. The future of the system of training, which would fit into the scheme student – technology – teacher, in which the teacher does not turn into a teacher-methodologist, technologist, and the student becomes an active participant in the learning process.
Complex Versus Flexible: CEO Cognition and Strategic Change in the Context of Advancing Tenure

We examine the effect of CEO tenure on the relationship between CEO cognition and strategic change. We focus on two fundamental cognitive map properties: cognitive complexity and cognitive flexibility. Our new methodological approach combines a topic modeling algorithm, trained on over 600 million words, with network analysis, to measure the complexity of CEO cognitive maps as well as their degree of flexibility over time. Our panel data analysis of 355 S&P 500 companies shows that the effect of cognitive complexity on strategic change becomes increasingly negative as the CEO´s tenure progresses. Conversely, the effect of cognitive flexibility on strategic change becomes increasingly positive over the course of the CEO´s tenure. Our study contributes to the behavioral strategy and strategic leadership literature by revealing how advancing tenure alters the relationship between CEO cognition and strategic action.