Reviewerly/exHarmony huggingface dataset:
The Improved Maneuvering Model Algorithm Based on Dynamic Feedback Neural Networks for Online Learning

Tracking of maneuvering targets plays an important role in sea battlefield situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, an hybrid filter algorithm based on dynamic feedback neural networks for online Learning is designed, which embedded the trained neural network with memory function into the state estimation step of the UKF filter to form a hybrid filter. Based on the input eigenvalues, the estimated error is predicted. This estimated error corrects the state estimation in real time and realizes the online monitoring of maneuvering. The simulation results show that the algorithm has a strong adaptability to the target maneuvering form, and has better performance in terms of convergence and filtering accuracy.
Ungrammatical Construction: An Analysis of the Causes and Types of the Ungrammatical Construction

The present research is about analysis on ungrammatical constructions encountered in the texts written by the 3rd semester undergraduate students of English Language Education Program of Tanjungpura University. The objective of the study is to describe the types of the ungrammatical constructions and the causes of the ungrammatical constructions. The method of research is descriptive. The research participants were 34 students of the 3rd semester undergraduate students of English Language Education Program of Tanjungpura University. The data were collected from corpus written by the students. The ungrammaticalities occurred in the students’ text. They were 496 errors encountered by the students. 312 errors related to phrasal errors and 184 related to clausal errors. The phrasal-related errors affected the area of noun phrase (37%), gerund phrase (13%), verb phrase (17%), infinitive phrase (12), prepositional phrase (12%), adjective phrase (7%), and adverb phrase (2%). The clausal-related errors were alternating form (49%), omission (33%), simple addition (15%), double marking (2) and misplaced subject and verb (1%). The sources of these errors were interlingual (2,9%) and intralingual (97,1%).
Research on Hybrid Encryption Algorithm Based on Data Transmission of Oil-Water Control System

With the progress of modern information technology such as big data and the Internet of Things, the progress of my country's shipbuilding industry has been driven, and the integration of ship transportation and Cyber-Physical systems represented by sensors, information, and communications will promote Ship intelligence has become the development trend of the shipbuilding industry. In the development of intelligent ships, the safety and efficiency of data transmission are very important. However, there are few studies on this aspect, and most of them use a single encryption method, which cannot guarantee high efficiency and high security in the process of ship data transmission. Therefore, based on the characteristics of RC2 and RSA, this paper combines the advantages of the two, and proposes a hybrid encryption algorithm based on RC2 and RSA. Experimental results show that the efficiency of this algorithm is significantly improved compared with other algorithms.
Implementasi Cloud Computing dengan Google Colaboratory pada Aplikasi Pengolah Data Zoom Participants

&#x0D; &#x0D; &#x0D; &#x0D; Since COVID-19 pandemic, many activities in the world of education, business, and religion, have become online. One of the online video conferencing applications that is often used is Zoom meeting. Due to the lockdown imposed by the government, the use of this application has increased. Even though the paid version has been used, there are still some difficulties for users to know the contributions of participants in meetings via Zoom Meetings, namely to know the data on the duration of participants’ attendance who attend meetings via Zoom and to sort out whether participants attend this meeting from beginning to end. Likewise, if the Zoom recording is stored on a computer or smartphone, it can be lost at any time due to various things.&#x0D; This problem can be solved by using Cloud Drive as the data repository and adding a python script to Google Colaboratory, so it can produce an application that can monitor the activity of each participant in the zoom participant data. After the implementation of the Google Colaboratory at ROCK church Makassar and by taking zoom data for 1 year, the results of the Google Colaboratory platform for the zoom participant data application, produce 3 graphs; a duration graph containing name and time duration data, a join leaves time detail graph containing data on names and hours of entry and exit, and a graph of the number of participants per session rundown of events. Additionally, by using this Google Colaboratory, storage becomes easier because it only requires internet, browser, and email.&#x0D; &#x0D; &#x0D; &#x0D;
A Secure electronic Voting Protocol Base on Threshold Proof

Electronic voting has gradually replaced traditional voting in certain scenarios because it enables voting to take place without limitations on time and location. However, confirming the validity of the ballot while preserving the anonymity of the voter's identity information remains a challenging issue. To address the aforementioned imperfection and the single point of failure in the registration authority, this paper proposes a secure electronic system based on threshold proofs and group homomorphic encryption, which effectively solves the issue of private key leakage in threshold signatures. In our scheme, the identity of voters is not bound to the ballot, and the voting result is publicly verifiable. In addition, we consider the balance between computational performance and the security of group homomorphic encryption. The experimental results show that selecting an appropriate cyclic group size can not only ensure the security of encrypted votes but also reduce the computational burden on the system.
Efektivitas Crossword Puzzle dalam Pembelajaran Bahasa Arab Sebagai Inovasi Pembelajaran Daring di Masa Pandemi Covid-19

The purpose of this study was to improve reading skills by using crossword puzzle in class X MIPA MAN 3 Jombang. This study used a quasi-experimental method with a non-equivalent control group pre-test post-test design. The population in this study was class X MIPA MAN 3 Jombang while the sample was X MIPA 7 as the control class and X MIPA 3 as the experimental class. Data were analyzed descriptively and inferentially. From the test data, it can be seen that the average value of the experimental class and the control class with the number of students 40 each class has increased. The average pre-test value of the control class is 56.75 and the average post-test value is 79.25 while the average pre-test value of the experimental class is 57.5 and the average post-test value is 87,5. Based on the results of the SPSS analysis 16.0, obtained sig. (2-tailed) of the experimental class and the control, class post-test is 0.00, which means lower than 0.05. So it can be concluded that H1 is accepted then there is a significant difference between the experimental class post-test and the control class post-test.
WOSCA: A Hybrid Algorithm of Whale Optimization Algorithm and Sine Cosine Algorithm for Large-scale Optimization Problems

The whale optimization algorithm (WOA) and sine cosine algorithm (SCA) exhibit limitations, such as premature convergence and local optima, for solving large-scale optimization problems. To address this problem, a novel hybrid algorithm called WOSCA is proposed. WOSCA leverages orthogonal Latin squares to obtain the initial population with balanced dispersion and neat comparability, and integrates the search mechanism of SCA into the WOA to enhance and balance the algorithm's exploration and exploitation. Moreover, to avoid falling into the local optimum and enhance the diversity of the population, a dynamic inertia weight strategy is introduced for an exhaustive search of nearby space. Twenty high-dimensional benchmark functions are selected to evaluate the effectiveness of the proposed method. The results demonstrate that WOSCA has better convergence accuracy and stronger robustness when solving large-scale optimization problems.
Blockchain-Based Data Security Sharing System

In view of the problems of privacy leakage and hacker interference with data sharing, a secure data sharing system based on blockchain for the Internet of Things is developed by using the encryption of encrypted text attribute policy library, synchronous aggregated signature and verifiable random features. The system manages user access by controlling the number of user visits, using algorithms such as synchronous aggregate signatures and verifiable random functions. The system can resist distributed denial-of-service attacks against cloud storage encrypted text, reduce decryption overhead in fine-grained secure data exchange, and improve audit efficiency.
Research on Image Style Transfer Algorithm of Cyclic Generation Adversarial Network with Multi-scale Residuals

Aiming at the problem of insufficient noise immunity of the generator model in the image conversion process in the cyclic generative adversarial network (CycleGAN), adding the Noise Robust Loss (NRL) function to the generator network can effectively improve the performance and robustness of the generator, making it more suitable for image conversion tasks in practical application scenarios. Aiming at the problem of image feature loss and underutilization of the generator model in the process of image conversion in the cyclic generative adversarial network, the Local Ternary Pattern (LTP) algorithm and the Multi Scale Residual Network (MSRN) are added to the generator network, in which LTP is used to extract image texture features. MSRN adaptively extracts image features of different scales through convolution kernels of different sizes, and then fuses these features to obtain more comprehensive image information. Through training and testing on three image datasets of horse zebra, apple orange, and summer winter, and comparing with CoGAN and SimGAN, it is proved that the multi-scale residual cyclic generative adversarial network proposed in this paper can generate higher quality and more visually attractive images.
Time-Optimal Exponential Trajectory Planning of Robotic Systems under Kinematic Constraints

This paper presents a novel time-optimal trajectory planning method designed for robotic systems with multi-degree-of-freedom (DOF). The proposed approach utilizes an exponential function as the basis for the velocity profile, enabling point-to-point trajectory generation with significant advantages in terms of smooth motion and ease of planning. The previous exponential trajectory planning approach appears to be slightly less efficient than traditional methods and could potentially compromise accuracy at waypoints, as it relies on constant parameters without considering time optimization. To improve motion accuracy and efficiency in the operation of multi-DOF robotic systems, the proposed method optimizes trajectories to minimize execution time while ensuring motion smoothness under the constraints imposed by the robot's kinematics. Numerical examples on a 3-DOF robotic arm demonstrate the superiority of the proposed method, showcasing improved position accuracy and motion quality. The presented time-optimal exponential trajectory planning method may offer a valuable tool for optimizing robotic performance, enhancing productivity, and enabling precise motion control in various robotic applications.
Facial Expression Recognition Based on CNN-LSTM

Facial expressions are the most informative part of human communication, and the recognition of facial expressions by computers has become a hot topic in the field of computer vision. Before the development of deep learning, manual determination of models was relied upon for facial expression classification. With the advancement of computer technology, CNN has gradually become the optimal model for facial expression recognition. However, since CNN only models local features and does not consider the temporal information contained in facial expression sequences, overfitting is prone to occur during the modeling process. In this paper, based on the above situation, LSTM, which performs well in handling sequential data, is introduced to construct a CNN-LSTM fusion model. CNN is used to capture local features, and LSTM is utilized to capture global features for optimizing CNN. To validate the effectiveness of the proposed model, experiments were conducted on the CK+48 dataset, which contains 750 images. Firstly, CNN was optimized through hyperparameter tuning, and after determining the optimal learning rate, CNN was used for classification prediction, with LSTM added as a supplement to CNN. Finally, it was found that severe overfitting occurred with a single CNN, but after adding LSTM, the accuracy on the test set increased to 84%, effectively compensating for the shortcomings of CNN.
Blockchain-based Off-chain Extension Model for Geographic Information Data

In recent years, the geographic information industry has been thriving; however, this growth has also gradually revealed certain security issues, including improper access control, data tampering, and forgery. Thus, to solve these problems, we propose a blockchain-based off-chain extension model for geographic information data, which uses blockchain and Interplanetary file system (IPFS) to realize the storage and sharing of geographic information data. In our scheme, each transaction on the blockchain needs to be confirmed by multiple signatures, so we use an aggregate signature scheme to reduce the size of the signatures and enable batch verification. Access control during data sharing is achieved through Ciphertext policy attribute based encryption. In order to improve the query efficiency of the blockchain, we design an MPT-based organizational storage of geographic information data. The experimental results show that the scheme can effectively reduce the cost of storing data on the chain while ensuring secure data sharing.
Research on CNN Model Based on Mill's Method

Convolutional neural networks (CNN) have significant advantages in solving artificial intelligence tasks, especially in the field of image classification. As the model of the complicated and large data set, CNN model training time is too long problem increasingly prominent, at present a lot of research work are committed to build a faster convergence speed, CNN model of single training time is shorter, but the model of training have been set in advance, only to achieve the set back propagation training times will stop. Therefore, an improved convolutional neural network based on Mill is proposed, and the corresponding Mill-loss model is designed, which is evaluated on the ResNet series with expanded convolution and the underlying LeNet model. The experimental results show that in the same environment, the CNN based on Mill-loss stops training within a few iterations after convergence, the model training time is significantly shortened and the accuracy is within the acceptable range.
Application of NoisyTune on Sentiment Analysis of BERT Model

In this paper, the online comment data of Python Programming course of Open Online Course Network of Chinese University is collected, and the data set is preprocessed by Word2vec training. By applying NoisyTune mechanism in the fine-tuning task of BERT model, the data set is trained, validated and tested, and it is verified that increasing noise can improve the Accuracy of data analysis, thus further improving the Accuracy of emotional tendency classification. Through experiments, NoisyTune mechanism can also be applied to other pre-training language models, which provides theoretical basis for the research of text Sentiment Analysis in other fields.
Multi-scale Face Detection Algorithm with Texture Feature Enhancement

A multi-scale face detection algorithm with texture feature enhancement is proposed for the difficult problem of tiny and occluded face detection in natural scenes. In order to obtain more fine-grained face texture features and enhance the ability of tiny face detection, the backbone network structure of the baseline algorithm was adjusted. The feature enhancement module was introduced to obtain rich contextual feature information and improve local semantic information characterization, which facilitated detection of tiny and occluded faces by fusing the feature of different receptive fields. To increase the neck network's ability, an improved shuffle attention module ISAM was adopted to perceive face detail information and facilitate the performance of occlusion and tiny face detection. Multi-task Associative Detector Head (MTADH) is used to learn the correlation between the classification task and the regression task to better utilize regression features and classification features to improve face detection results. By controlling the width and depth of the detection networks, two detection networks with different sizes, MFDTFE-M and MFDTFE-S, are constructed. On the WIDER FACE validation dataset, the average precision of the two detection networks on the Hard subset is improved by 2.99% and 2.77% relative to the m-model and s-model of the baseline algorithm, and the performance of the tiny and occluded face detection is significantly improved.
Domain Knowledge Graph Completion Based on Attribute Hierarchy

Knowledge graph completion is to solve the problem of lack of entities and relations in knowledge graphs. Existing Knowledge graph completion methods mainly embed entities and relations into latent vectors, and numerous researches have taken the rich relationships in knowledge graph into account such as category, entity, relation and semantic. However, only a few studies consider the relation between attributes, which is the basis of describing the entity. This paper proposes the Attribute Hierarchy Knowledge Graph Completion (AH-KGC) method, aiming at leveraging the attribute relation to find the missing obligatory property of entities. Primarily, in AH-KGC, we have discussed the attributes prerequisite relation, which can be described as a tree-like hierarchical structure, and then adopt the search algorithm of preorder traversal based on the hierarchy to find out the missing attributes. Specifically, we prove that attribute prerequisite is a special case of implication, thus can obtain attribute hierarchy from implications relation, which can easily be obtained in much mature research such as expert systems and make full use of the knowledge in various fields to make up for the vacancy of domain knowledge in the knowledge graph. The experiment has been performed on the CN-DBpedia and Probase datasets. The result demonstrates that AH-KGC can effectively complete the missing attributes of entities in knowledge graphs and achieve 100% accuracy under our evaluation system.
Aerial roaming aerial photography path planning based on areas of interest

In virtual reality, traditional roaming methods are limited to a single ground roaming. In order to enrich tourists' roaming experience, this paper proposes an aerial photography path planning method based on areas of interest. First, by obtaining the characteristic information of the landmark, including the outline of the landmark and the middle area of the top surface of the landmark, the corresponding saliency value of the landmark surface is calculated. Then, a landmark-centered viewpoint quality field is constructed to guide local virtual camera motion. In this process, the saliency value is combined with the center composition and the rule of thirds composition in photography techniques to calculate the viewpoint quality score. Finally, a diagonal composition method was adopted to provide an excellent visual experience for flying between landmarks. Experiments show that the aerial photography path obtained by this method allows users to appreciate the beautiful scenery of the city from an aerial perspective in a short period of time, and has strong practicability and applicability.
The Role Of Teachers Facing Digital Competence Under The Influence Of Ict

The central objective of this article is to delve into the role of educators in the teaching-learning processes, where digital competence, driven by the presence of Information and Communication Technologies (ICT), becomes imperative. This approach was undertaken from a qualitative perspective, following the guidelines of the interpretative paradigm and the concrete actions of the hermeneutic method. The selected context was the San Francisco de Sales Educational Institution in Cúcuta, North of Santander, Colombia, with the participation of five secondary school teachers (said sample obtained high density and complies with saturation). The result highlights a notable tension between the expectation of adopting an education with a technological focus that facilitates the didactic organization of curriculum content in secondary education, and the reality of an educational environment influenced by traditional approaches and a lack of enthusiasm towards innovation and change. It is concluded that the holistic training of teachers is what enables an appropriation of ICT and their application in pedagogical processes. This comprehensive pedagogical approach goes beyond mere acquisition of technical skills to embrace a deep and contextualized understanding of how ICT can genuinely enhance teaching and learning processes.
Developing a governance framework for the metaverse: an integrative literature review

This integrative literature review described the views of metaverse experts on how business leaders and policymakers can collaborate to develop a governance framework for the global metaverse to accelerate business innovation. This study was framed by three key concepts: Rogers's concept of diffusion of innovations, Ball's concept of the metaverse, and Fernandez and Hiu's concept of privacy, ethics, and governance in the metaverse. While there has been an increased interest in the research on the metaverse, there is still a significant gap in the literature on empirical research on the governance structure of the metaverse. This study's outcome may contribute to positive social change and the management field by presenting practical information on developing a governance framework to regulate the metaverse.
Special affine biorthogonal wavelets on R and logarithmic regression curves

In the article ?Special affine multiresolution analysis and the construction of orthonormal wavelets in L2(R)?, [Appl Anal. 2022; D.O.I: 10.1080/00036811.2022.2030723], we introduced the notion of multiresolution analysis (MRA) in the realm of the special affine Fourier transform. In continuation to the study, our aim is to present the construction of special affine biorthogonal wavelets in L2(R). Besides, we provide a complete characterization for the biorthogonality of the translates of the scaling functions of two special affine MRA?s and the associated special affine biorthogonal wavelet families. We show that the wavelets associated with the biorthogonal special affine MRA?s are also biorthogonal in nature. To extend the scope of the present study, we present the biorthogonal special affine MRA and its biorthogonal properties on a logarithmic regression curve C .
Open development

The Elgar Encyclopedia of Development is a ground-breaking resource that provides a starting point for those wishing to grasp how and why development occurs, while also providing further expansion appropriate for more experienced academics.
Digital inclusion

The Elgar Encyclopedia of Development is a ground-breaking resource that provides a starting point for those wishing to grasp how and why development occurs, while also providing further expansion appropriate for more experienced academics.