Reviewerly/exHarmony huggingface dataset:
Research on the rapid assembly algorithm of the wireless ultraviolet cooperative UAV formation

Aiming at the problem that the strong electromagnetic interference environment restricts the communication of the Unmanned Aerial Vehicle(UAV) formation, which affects the rapid assembly of the UAV formation, a wireless ultraviolet cooperative UAV formation rapid assembly algorithm is proposed to realize the fixed-point assembly of the UAV formation in the airspace. First, the Ultraviolet(UV) light beacon model is used to realize the information interaction of the UAV formation. secondly, the UV four-node positioning algorithm is used to realize the three-dimensional positioning of the UAV formation. Combined with the method, the UAV formation can reach a consensus speed when assembling. The simulation results show that when there are more than 6 reference nodes, the three-dimensional space positioning accuracy can reach 96%. The assembly algorithm can realize the fixed-point assembly of the UAV formation, and can maintain the circular motion trajectory after the assembly is completed.
Gaming method as a perspective educational method of teaching English at Pedagogical Institutes

The purpose of the investigation is to examine the gaming method from the perspective of the educational paradigm and to identify the potential for its use in higher education.The scientific novelty of the research consists of systematizing the advantages of using the method of gaming for educational purposes and of the selection of gaming tools, realizing the didactic principles noted by the authors of the article.The result of the investigation is the producing of methodological recommendations set for teachers of pedagogical higher educational establishments, aimed at the qualitative integration of the gaming method into the educational process and the creation of an effective environment for teaching students by the gaming system.
Women Online: A Study of Common Service Centers in India Using a Capability Approach

Income-generating activities performed by women are an effective way to reduce gender deprivations and disparities. In the constrained familial and community settings of developing economies, the online platform can be an appropriate means for women to carry out economic activities. In this context, certain important initiatives taken by the Government of India such as the creation of Common Service Centers are worth studying. We critically evaluate such revolutionary online platform-based entrepreneurial initiatives using the capability approach developed by Amartya Sen. We examine how women-run businesses use the online platform and what determines their success, inputs, capabilities, and conversion factors through a case study method. Further, enterprise-level National Sample Survey Organization data at the all-India level are analyzed to show that the states that have a higher level of gender inequality are also the regions with a lower level of information and communication technology usage by women-run enterprises.
The Effect of Using Word Flipchart Media on Reading Ability in Group A Students in Kindergarten

The purpose of this study was to analyze the influence of word flipchart media on the reading ability of group A children at Dharma Wanita Persatuan Tlasih Kindergarten, Tulangan District. This study uses a quantitative method of pre-experiment type one-group pretest-posttest design. The population of this research is the children of group A TK Dharma Wanita Persatuan Tlasih, Tulangan District with saturated sampling technique with 16 students. The data collection technique in this study used the results of the initial reading ability test. The data analysis technique used the dependent t test with the research results tcount = 11.30 and ttable = 2.131 so that tcount &gt; ttable, then H1 was accepted and H0 was rejected. This shows that there is an effect of word flipchart media on the reading ability of group A children at Dharma Wanita Persatuan Tlasih Kindergarten, Tulangan District. Keywords: Word Flipchart Media, Reading Ability.
Improve Reading Skills through the Medium of Clever Box on the Age 4-5 Years in Kindergarten

Ability to read has a role which is important for a child's life early because introduce letters to children is the initial step son is able to read and to make a word. This research aims to know the application and the results of the media “Clever Box” against the enhancement of reading in a child group A. This research uses the Research Action Class as one approach to improve the ability to read the child. Implemented in a 2-cycle is divided into three levels namely prasiklus, cycle I and cycle II to get the result with the expected. In the pre cycle yields the value of the prosentase range of actions is as big as 39,66% whereas in cycle I prosentase increased by 61,15% until acquired prosentase value to be 60,34 which means no avail. Then research further by giving the action in cycle II, after having done research on the cycle II of this value prosentase kecapain action be 84,49%, up from the procurement value prosentase in cycle I and cycle II experienced an increase as large as 23,34%. With this can be seen from the comparison between cycle I and II for the research progresses. From the results obtained there is an increase that proves that with the use of the media “Clever Box” can improve the ability of the child's reading.
Research Limitations and Future Directions on the Effect of Educational Practice on the Cultivation of Teachers' Professional Competency of Education Major Students: A Case of M University in China

A very important practical teaching activity for Chinese education major students during their four years of undergraduate study is educational practice.The process of education practice for education major students is a learning activity that will provide hands-on experience of the whole process of education and teaching practice, including participating in practical teaching of courses, classroom management, active learning and communication, relevant competitions, training and activities, etc.In this paper, a semi-structured interview questionnaire was used to interview relevant personnel at M University in China.The interviewees included eight instructors who were involved in the training of education majors and eight education graduates (currently primary and secondary school teachers) who had graduated for two to five years.The analysis of the interview results showed that: education practice of education major students can enhance education major students' professional competence of teachers; education majors students' education practice, training competitions and other activities in their four years of undergraduate study are beneficial to their career development.Therefore, strengthening education practice and training competitions for education major students has a positive impact on the cultivation of education major students, and universities where education major students are studying should broaden effective ways to strengthen links and cooperation with local governments and primary and secondary schools, education major students use their initiative, innovative educational practice training using smart education resources and more, so as to play an active role in cultivating more outstanding education major graduates.
The Concept of Computational Linguistics

The field of computational linguistics has developed in recent years.It has become an essential area of industrial growth.Computational linguistics involves both sides engineering and scientific.The engineering side is regularly named natural language processing.This component includes creating computer programs that perform beneficial language-related tasks, such as machine translation, question-answering, and summarization.Natural language processing, like many engineering fields, is based on the findings of numerous scientific sides (Johnson M. Oct. 2011).This study tends to give a brief idea about Computational Linguistics specifically its definitions, development, and applications.
Fashion Image Retrieval based on Parallel Branched Attention Network

With the increase in vision-associated applications in e-commerce, image retrieval has become an emerging application in computer vision.Matching the exact user clothes from the database images is challenging due to noisy background, wide variation in orientation and lighting conditions, shape deformations, and the variation in the quality of the images between query and refined shop images.Most existing solutions tend to miss out on either incorporating low-level features or doing it effectively within their networks.Addressing the issue, we propose an attention-based multiscale deep Convolutional Neural Network (CNN) architecture called Parallel Attention ResNet (PAResNet50).It includes other supplementary branches with attention layers to extract low-level discriminative features and uses both high-level and low-level features for the notion of visual similarity.The attention layer focuses on the local discriminative regions and ignores the noisy background.Image retrieval output shows that our approach is robust to different lighting conditions.Experimental results on two public datasets show that our approach effectively locates the important region and significantly improves retrieval accuracy over simple network architectures without attention.
Social Information Popularity Prediction based on Heterogeneous Diffusion Attention Network

Information popularity prediction on social media platforms is a valuable and challenging issue.However, existing studies either neglect the correlation among different cascades, or lack a comprehensive consideration of user behavioral proximity and preference with respect to different messages.In this paper we propose a graph neural network-based framework named HeDAN (heterogeneous diffusion attention network), which comprehensively considers various factors affecting the information diffusion to predict the information popularity more accurately.Specifically, we first construct a heterogeneous diffusion graph with two types of nodes (user and message) and three types of relations (Friendship, Interaction, and Interest).Among them, Friendship reflects the strength of social relationship between users, Interaction reflects the behavioral proximity between users, and Interest reflects user preference to messages.Next, a graph neural network model with hierarchical attention mechanism is proposed to learn from these relations.Specifically, at the nodelevel, we utilize the graph attention network to learn the subgraph structure and generate the representations of nodes under each specific relationship.At the semantic-level, we distinguish the importance of different nodes in different relations via multihead self-attention mechanism.Extensive experimental results on three datasets show the superior performance of our proposed model over the state-of-the-arts.
Two-Stage AST Encoding for Software Defect Prediction

Software defect prediction (SDP) can find potential containing defect modules, which assists software developers in allocating limited test resources more efficiently.Because traditional software features fail to capture the semantics of source code, various studies have turned to extracting deep learning features.Existing related approaches often parse the program source code into Abstract Syntax Trees (ASTs) for further processing.However, most of these approaches ignore AST nodes' hierarchical and position-sensitive structure.To overcome the aforementioned issues, a two-stage AST encoding (TSE) method is proposed in this paper for software defect prediction.Experiments on eight Java open-source projects showed that our proposed SDP method outperforms several traditional methods and state-of-the-art deep learning methods in terms of F-measure and MCC.
Institutional Restructuring and Reforms for National Development

This paper argues that weak national institutions cannot be relied upon to produce successful restructuring and reform packages that can lead to accelerated national development and higher living standards for the Nigerian people. The paper further identifies weak national institutions, the pursuit of pecuniary individual and group interests, ethnic cleavages and intra-ethnic rivalries among the elite class, including the extractive nature of politics and economic pursuits, as major factors militating against successful restructuring and meaningful institutional reforms in Nigeria, which are hinged on accelerated technological innovation, political development and wealth creation. The paper concludes that only strong and functional institutions, inclusive political and economic policies, and innovative leadership can bring about accelerated national development in Nigeria.
Surveillant: a supervision mechanism between blockchains for efficient cross-chain verification

Blockchain interoperability, which refers in particular to the ability to access information across blockchain systems, plays the key role for different blockchains to communicate with each other, and further supports the superstructure built on top of the cross-chain mechanism.Nowadays, blockchain interoperability technology is still in its infancy.The existing crosschain scheme such as BTCRelay requires that the smart contract in a blockchain to download and maintain block headers of the other blockchain, which is costly in maintenance and inefficient to use.In this paper, we propose a supervision mechanism between blockchains, called Surveillant.Specially, the new entities called dual-functional nodes are introduced to commit the real-time information from the blockchain under supervision to the supervising blockchain, which enables users to have efficient cross-chain verification.Furthermore, we introduce Merkle mountain range for blocks aggregation to deal with the large-scale committing data.We propose the design of long orphan branch counter to trace the bifurcations in the blockchain under supervision.The existing incentive mechanism is improved to encourage the behaviors of dual-functional nodes.In Surveillant, the analysis and experimental results demonstrate that users are able to have efficient cross-chain verification with low maintenance overhead.
Community Detection using Closeness Similarity based on Common Neighbor Node Clustering Entropy

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity.However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs.To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA.Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed.Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified.Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities.Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization.Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.
Recovery of underwater images based on the attention mechanism and SOS mechanism

Underwater images usually have various problems, such as the color cast of underwater images due to the attenuation of different lights in water, the darkness of image caused by the lack of light underwater, and the haze effect of underwater images because of the scattering of light.To address the above problems, the channel attention mechanism, strengthen-operate-subtract (SOS) boosting mechanism and gated fusion module are introduced in our paper, based on which, an underwater image recovery network is proposed.First, for the color cast problem of underwater images, the channel attention mechanism is incorporated in our model, which can well alleviate the color cast of underwater images.Second, as for the darkness of underwater images, the similarity between the target underwater image after dehazing and color correcting, and the image output by our model is used as the loss function, so as to increase the brightness of the underwater image.Finally, we employ the SOS boosting module to eliminate the haze effect of underwater images.Moreover, experiments were carried out to evaluate the performance of our model.The qualitative analysis results show that our method can be applied to effectively recover the underwater images, which outperformed most methods for comparison according to various criteria in the quantitative analysis.
Reinforcement learning algorithms using for agent behaviour modelling and researching

Achieving autonomy and rational behaviour, especially when the environment is complex, partially observable, dynamic or unfamiliar, requires the inclusion of machine learning methods. This article presents the latest and most widely used model-based and model-free reinforcement learning algorithms and their applications. In model-based reinforcement learning algorithms, the learning agents have knowledge of their environment, which leads to faster learning and faster building of optimal policies. In model-free reinforcement learning algorithms, agents build optimal policies by interacting with the environment, which makes their training slower. On the other hand, their advantage is that they can work with unfamiliar and dynamically changing environments. Some hybrid reinforcement learning algorithms are also considered.
Sistem Informasi Wisata Kuliner di Kabupaten Lebak Berbasis Web

Technological developments are increasingly rapid and easy to use, one of which is the development of information technology which can also contribute to the existence of an area that has culinary tourism potential. In the business world, the role of technology is very important, and it can be said that it has become a primary need for entrepreneurs at this time. Likewise for culinary, which has utilized the internet network to present various information, both from information on places, menus, and prices in the field of information technology. However, for culinary information in Lebak Regency on a website, while this is still incomplete and there is no website that specializes in culinary tourism in Lebak Regency. Therefore, a web-based culinary tourism information system application design is needed in Lebak Regency to make it easier for the public to find culinary information in Lebak Regency. System design and implementation will begin with system requirements analysis or known as system requirements analysis. This is intended to obtain the appropriate system specifications. In the design stage. Unified modeling language (UML) is used to clearly describe business processes. The test results of the website-based culinary tourism information system in Lebak Regency show that the features contained in the application can work well such as managing culinary data, managing admin data, managing member data. This information system has three users on the system, namely admin, member and user.
Strategie argomentative dei leader politici italini nelle lettere ai giornali

Political discourse, in years marked by the transition from the Second to the Third Republic, appears to be dominated by the imperative of animos impellere. As recent studies on Italian political language have pointed out (Librandi and Piro 2016, Cortelazzo 2016, Antonelli 2017, Ondelli 2021), political leaders achieve this objective by increasing their contentious tone and substantially lowering their linguistic register, especially in face-to-face interaction or in the social media. An exception to this trend can be found in the letters written to newspapers by political leaders to clarify their political positions or to reply to polemical positions expressed by the newspaper itself. Those letters are short argumentative texts, addressed to a wide audience, particularly suitable for political positioning or repositioning in crucial moments of the debate or in view of possible changes in the political situation. This article examines the letters of four protagonists of Italian politics (Silvio Berlusconi, Matteo Renzi, Giorgia Meloni and Matteo Salvini), using the discourse analysis theories from the French School.
Online review analysis on various networks’ consumer feedback using deep learning

In order to understand the application of deep learning in consumer online review analysis, the text content of online review and the impact of national culture on it are studied. A consumer online comment analysis model based on deep learning is constructed and utilised using edge computing. With the online reviews of mobile phone and computer products as the case, the analysis is made. The data of online reviews on various network platforms is obtained. The model based on edge computing is used for analysis, and the results of product feature sentiment analysis are obtained. The results show that the deep learning model can achieve effective product feature extraction and high accuracy sentiment analysis, which is reasonable and effective in practical application.
Revival and distribution of Einstein–Podolsky–Rosen steering of a four-mode cluster state in noisy channels

Einstein–Podolsky–Rosen steering is an important resource for constructing secure quantum communication networks. The quantum states carry information transmitted among the spatially separated nodes through the quantum channels, and the steerability of quantum states will reduce or even completely disappear due to the noise in the channels. We theoretically studied the revived steering properties of the quadripartite quantum state. The steering properties versus transmission efficiency and noise amplitude were investigated. Our studies show that when the noise in the channels reaches a certain value, it will lead to the sudden death of steering, and the steerability can be revived over a large range of values by establishing correlated noisy channels. Different types of monogamy relationships are verified in our scheme. These results provide a theoretical foundation for the construction of secure quantum networks.