Reviewerly/exHarmony huggingface dataset:
An Improved Analysis of Secured Permutation and Substitution based Image Encryption

The transmission and storage of digital data raises serious security concerns as information technology evolves at a breakneck pace. To ensure the safety of the transferred data, security methods must be put in place. Encrypting an image is a method of protecting sensitive data by converting it into an unrecognizable format. The procedure includes access control, privacy, validation, and copyright protection. Cryptography, steganography, and watermarking are three distinct methods to prevent unauthorized access to digital data. Of these three methods, cryptography has emerged as one of the most important ways to ensure complete safety. Therefore, a secure and efficient cipher algorithm is required for trustworthy communication. In this work, we offer a practical Secured Asymmetric Image Cipher (SAIC) Algorithm for encrypting images with a secret key of arbitrary length. At first, the KG algorithm creates two unique keys. Both the encryption and decryption processes require a key. The experimental results reveal that the encrypted image lacks the original image's independence (NPCR 99.89%, UACI 36.89%). The suggested approach has a high encryption rate, can be implemented easily, and is computationally secure. The reproduced data validates the safety and practicability of the proposed architecture.
A study of -ni naru and -to naru in Japanese: Based on corpus

The verb NARU is a change verb that indicates a change in state or shape and is used in two forms: -ni naru and -to naru. Until now, the differences in meaning and usage between the two forms have been approached mainly from an expressive or semantic perspective. This paper describes the difference between the two forms by using a corpus to analyze the preceding nouns just before -ni naru and -to naru. The bias in the distributions of -ni naru and -to naru is shown by Yahoo! Answers (OC), blogs (OY), and white papers (OW), respectively. For reference, not a single example of -ni naru is detected in the law (OL). The semantic characteristics of -ni naru are: (i) the change occurs naturally in the course of time, and the focus is on the process of change. (ii) Pre- and post-phases of change are envisaged. (iii) The noun that precedes it is more common in words of Japanese origin than in Sino-Japanese words, and it also receives interrogatives. (iv) It draws the aspect of change to one
On preparing future teachers for personal development training

The questions of what and how to teach are traditional in education. Since knowledge in modern pedagogical science is concentrated around approaches, familiarity with them should form a meaningful basis for the training of future teachers. Pedagogy in its essence is the science and art of managing the processes of development and formation of an individual’s personality. This prompts us to pay attention primarily to instrumental approaches: personal, situational, activity-based and environmental. Their strength lies in the means to which they “owe” their name. However, while familiarizing themselves with these approaches, students face a number of difficulties. They are caused by the following facts: the place and significance of each approach in the system of pedagogical knowledge is uncertain; the methodology and theory of approaches are detached from technology and techniques; predisposition and willingness of students to study the content of the approaches in a certain order are neglected; departments have a limited capacity for carrying out training in accordance with the approaches themselves. These reasons make it difficult to understand the role of each approach in personal development training. They prevent students from mastering the theory and instrumental base of the approaches. The latter prevents them from starting personal development training. The ideas formulated in this article are the author’s answer to these problems. In addition, the historical precedent given in the article, which later developed and took shape in the doctrine of the environmental approach, is intended to help comprehend the situation with the organization of personal development training in pedagogical universities.
BiDAF model in sentiment analysis task

Sentiment analysis is a critical job in natural language processing. Controlling and evaluating customer feedback on their goods is a task that companies are especially interested in. For reading comprehension problems including attention processes, the BiDAF model is developed. Attention processes have recently been expanded and effectively used for natural language processing problems. In this study, we use the BiDAF model to perform sentiment analysis on Amazon product evaluations at the sentence level. The BiDAF model is a multilayered processing model that reflects context at multiple levels and uses the BiLSTM model. Furthermore, we investigate the sentence's attention weight distribution using the attention mechanism. With a recall measure, the model achieves an accuracy of up to 99.9%. We discovered that the attention weights of important phrases are equivalent to, if not higher than, the attention weights of sentiment words in the sentence.
Improving the roles and tasks of primary school administrators in the implementation of the 2018 General Education Program

The context of the implementation of the renovation of the 2018 General Education Program has been posing a requirement to improve the roles and responsibilities of educational institution management staff, including primary school administrators, following the trend of decentralization of management, strengthening the autonomy and accountability of schools in implementing educational plans. However, the current situation of the quality of management staff is still inconsistent with the program requirements. Stemming from the actual context, the article proposes measures to improve the roles and responsibilities of primary school administrators to meet the requirements of the 2018 General Education Program.
Generalizing FAIR

by Daniel S. Katz and Michelle Barker Most researchers and policymakers support the idea of making research, and specifically research outputs, findable, accessible, interoperably, and reusable (FAIR). The concept of FAIR has been well-developed for research data, but this is not the case for all research products. This blog post seeks to consider how the application of FAIR to a range of research products (beyond data) could result in the development of different sets of principles for...
VERIFICATION OF OFFLINE SIGNATURE USING MACHINE LEARNING

Every person has a distinctive signature which is primarily used for personal identification and to confirm the authenticity of significant papers or legal transactions. The process of implementing offline or online signature verification will depend on the application. Online systems make advantage of dynamic data from a signature which is recorded while signing. Offline systems utilize a signature's scanned image for operation. Initially system is trained using a database of signatures gathered from individuals whose signatures it must authenticate. The lack of sufficient data for testing is the main issue with signature verification. High accuracy must be used in the development of signature security. In this paper, Offline signature verification that makes use of grid-based extraction of features using ML and CNN have been introduced.
Software and repositories in the context of FAIR

The latest Research Software Alliance (ReSA) newsletter includes the fact that: The 2018 European Commission report, Turning FAIR into Reality, concludes that FAIR digital objects (including software) need to be supported by metrics, incentives, skills and FAIR services that provide persistent identifiers, metadata specifications, stewardship and repositories, actionable policies and Output Management Plans. This has made me think about the role of software repositories in encouraging...
USE OF SUPER-RESOLUTION IN TEXT-DETECTION

Text images are used to convey crucial information in a variety of applications, but it can be hard to recognise low-resolution text images. The majority of previous solutions use a cascaded technique to handle this issue in two steps: high-resolution text recognition and image super-resolution. It is quite difficult to recognise text from a poor-clarity image. The goal of super- resolution is to transform low-resolution images into high-resolution ones. The study examines, contrasts, and compares the technological difficulties, approaches, and efficacy of super-resolution in text detection. This research looks into the role of super-resolution in low-resolution picture text recognition.
COMPARATIVE ANALYSIS EVALUATION OF INTELLIGENT FACE RECOGNITION

Intelligent face recognition has become a basic technology in work and life. This technology has been immensely used in various products and is well known by people in all sectors. However, the intelligent Face recognition systems developed at present lacks a universal method, and the designed system cannot be applied to all types of products. In order to improve face recognition systems and get better accuracy the main aim is to study and understand face recognition based on different algorithms to find out which is the best suitable method for the same that can be used universally. The study is based on the two widely known algorithms called PCA- Principal Component Analysis, LDA-Linear Discriminant Analysis, comparing the two in terms of face recognition accuracy on a popular dataset called ORL.
SURVEY ON FACE DETECTION AND RECOGNITION ALGORITHMS USING DEEP LEARNING

A facial recognition system uses a number of algorithms to recognize faces in digital photographs, identify people, and then confirm the authenticity of the acquired images by comparing them to facial images that have been saved in a database. Biometric technology is based on facial features of a person. Face detection and Recognition are major concerns in the area of biometric based systems and purposes. This process must ensure recognition accuracy and minimum processing time. Some cutting-edge techniques allow it to be retrieved more quickly in a single scan of the raw image and lie in a smaller dimensional space while effectively keeping face information. The techniques for face detection and recognition are classified on the bases of their target application. Also, the techniques are classified and analysed on the bases of their working domain as spatial, frequency, integrated and hardware support. Face detection is a challenging topic in computer vision because the human face is a dynamic object with a great degree of diversity in its appearance. There have been many different ways put forth, from straightforward edge-based algorithms to composite high-level systems leveraging cutting-edge pattern recognition techniques. With the help of biometrics, a facial recognition system can extract facial details from a picture or video. The data faceprint stored via facial traits is compared by the face recognition software using deep learning algorithms. Among them, face detection is a very potent tool for face recognition, image database management, human computer interface, and video surveillance. Face recognition is a rapidly developing technology that has been used extensively in forensics for purpose including criminal identification, airport security, and controlled access.
COMPARATIVE ANALYSIS OF CONVOLUTIONAL NEURAL NETWORK MODELS APPLIED IN HUMAN BEHAVIOUR RECOGNITION

Human behaviour recognition is a crucial area of scientific research in the science of computer vision that has significant applications in a variety of industries, including intelligent surveillance, smart homes, and virtual reality. Traditional manual approaches have a hard time meeting the demands of high recognition accuracy and applicability in the contemporary complicated environment. Deep learning's arrival has opened up new avenues for behaviour recognition research. The major focus of this paper is behaviour recognition using convolutional neural networks (CNN). Before discussing and analysing the classical learning methods and deep learning methods of behaviour recognition, the research context and importance of behaviour recognition are first introduced. Based on the convolution neural network designed for the specific human behaviour in public areas, we develop a series of human behaviour recognition systems. In order to extract behaviour or characters of the body, the video and images of human behaviour data set will be processed. Then the planned convolution neural network is trained with the training data sets, and the deep learning network is built. Finally, using the developed network model, the numerous sample behaviours are categorized and recognized, and the recognition outcomes are then evaluated. The findings demonstrate that CNN is capable of studying human behaviour models automatically and recognizing human behaviours without the need for manually annotated training.
A Study of ‘Comparative Poetry Talks’: Focusing on the Classification and Itemization of Poetry Talks in Sihwachonglim

이 연구는 기존에 논의하였던 「데이터 기반 시화 연구 방안 모색」에 더하여, 시화를 더욱 체계적으로 데이터화하는 방안을 시화총림이라는 표본 자료의 분석을 통해 논의해 보고자 한다. 구체적으로는 본 연구에서 전면에 두고 사용한 ‘比較詩話’라는 용어가 어떠한 문제의식에서 사용되었으며 어떤 부분에서 효용이 있는지를 논하고, 예시를 통해 구체적으로 세분화하여 살피는 작업을 선행한 후 각각의 특징적인 부분을 들어 시화를 효율적으로 데이터화하는 방안에 대해 논의하여 보고자 한다. ‘비교시화’의 구분 및 세부 분류를 통해 시화총림 데이터베이스의 구축과 관련하여 특히 그 ‘비평’ 항목의 세부 항목을 재조직하고 그 의의를 살피는 데에 이 연구의 의의가 있다. In addition to the previously discussed “A Foundational Design for Creating a Poetry Talk (詩話) Semantic Data Archive,” this study aims to explore ways to systematically collect data on Poetry Talk using sample data called ‘SihwaChonglim.’ Specifically, we will discuss the significance of the term ‘Comparative Poetry Talk’ used in this study, its usefulness, and ways to efficiently collect data on Sihwa by examining each characteristic component through detailed examples. The study is particularly significant in that it reorganizes the detailed elements of the ‘Critique’ section and explores their relevance to the development of the ‘SihwaChonglim’ database through the classification and detailed categorization of ‘Comparative Poetry Talk’.
A Fault Diagnosis Knowledge Reasoning Model based on GAT and RotatE

As information science improves by leaps and bounds, knowledge graph has been generally applied in all sorts of domains. By building knowledge graph in the industrial field, it can assist staff to complete fault diagnosis and repair. However, there are some problems in existing knowledge graph in the industrial field, such as sparse knowledge distribution and lack of relations between entities, which make it difficult to use knowledge effectively. Based on these problems, this paper proposes a new knowledge reasoning model based on graph attention network and knowledge embedding model by relational rotation. Graph attention network aggregates neighbor node features with different weights to a new node; knowledge embedding model by relational rotation calculates the dot product of entity vectors and relation vectors, obtains the potential relation characteristics between the triplets. Finally, this paper verifies on two knowledge graph datasets in the industrial field, furthermore experimental results reveal superiority of new model.
Improved Preintegration Method for Vehicle Navigation Based on Factor Graph Optimization

To solve the problem that the IMU nodes of the traditional factor graph are unable to be corrected in time and the positioning accuracy decreases in the condition of satellite failure, an improved preintegration method for the vehicle navigation system is proposed. Different from the current factor graph optimization method, the proposed method that involves corrective parameters of visual sensors is introduced. The structure of the system is changed when GNSS is unavailable and the calculated information of Lidar is used to compensate for IMU errors. The sliding window is used to improve the real-time performance of the system. The experimental results show that the proposed algorithm can improve the positioning accuracy of vehicle navigation when GNSS is unavailable. Compared with the traditional factor graph integrated navigation algorithm, the positioning accuracy of the proposed preintegration method is improved by more than 16% in GNSS failure environments.
Swap Softmax Twin Delayed Deep Deterministic Policy Gradient

Reinforcement learning algorithms have attained noteworthy accomplishments in the field of continuous control. One of the classic algorithms in continuous control, the DDPG algorithm, is widely used and has been shown to be susceptible to overestimation. Following this, the TD3 algorithm was introduced, which integrated the notion of double DQN. TD3 takes into account the minimum value between a pair of critics to restrict overestimation. Nevertheless, TD3 may lead to an underestimation bias. To mitigate the impact of errors, we present a novel approach by integrating Swap Softmax with TD3, which can counterbalance the extreme values. We assess the efficacy of our proposed technique on continuous control tasks that are simulated by MuJoCo and provided by OpenAI Gym. Our experimental findings demonstrate a significant enhancement in the performance and robustness.
Risk-Aware Path Planning Using CVaR for Quadrotors

In the presence of obstacles with static position uncertainty, a risk-aware path planning method using the conditional value-at-risk (CVaR) is proposed. Given the current position of the quadrotor, CVaR can effectively quantify the risk of collision with the static uncertain obstacle whose center of mass (CoM) follows a joint normal distribution. As a specific application of CVaR, the CVaR constrained A*(CVaR-A* for simplicity) algorithm is designed to search for the optimal path while ensuring the safety of the quadrotor. The simulation results are presented to indicate the feasibility and effectiveness of the proposed CVaR-A* algorithm.
A new type of video text automatic recognition method and its application in film and television works

In the current post-production of film and television, editing and school teams need to identify each frame of image in the video and modify the sensitive words in it, which consumes a lot of manpower, material resources and financial resources. This article proposes an image recognition method based on attention mechanism to overcome this problem, which can quickly recognize specified texts in videos. Using the convolutional neural network and codec as the framework, the target text is used as the operation object of the attention mechanism, so as to realize the rapid recognition of pictures containing the target video text. To validate the proposed method, a TV series is taken as an example to test the characters involved in the video, and the results show the effectiveness of the proposed video text automatic recognition method.
Multi-UAV online path planning algorithm based on improved Hybrid A*

Real-time path planning of multi-UAV includes obstacle avoidance and anti-collision, which is an important condition to ensure the coordinated operation of multi-UAV. This paper studies a multi-UAV online path planning algorithm based on improved Hybird A *. Each UAV uses the Hybird A * algorithm for distributed path planning. Through the collision risk assessment of the planned path, if there is a collision risk, the path adjustment is based on the defined UAV priority level and interactive information. In this paper, the existing two-dimensional velocity obstacle model is improved, and a three-dimensional velocity obstacle model suitable for 6-DOF UAV is constructed to calculate the safe speed range of UAV. As the optimization constraint condition, the path is adjusted based on the potential field method to achieve the effect of obstacle avoidance and collision avoidance. Finally, the simulation system and experimental platform are built to verify the real-time and stability of the proposed algorithm.
A GPR-Aided Method for Collaborative Relative Navigation for Multiple Unmanned Aerial Vehicles During Ranging Measurement Outages

To maintain reliable relative navigation accuracy of unmanned aerial vehicles (UAVs) during range measurement outages, a GPR-aided ranging method is proposed to assist the extended Kalman filter for measurement updates. Specifically, when the on-board ranging measurement is available, a Gaussian process regression (GPR) algorithm is used to train the mapping model between acceleration, angular velocity of the inertial navigation system (INS), relative position, and the ranging distance. When ranging measurement is unavailable, the trained GPR algorithm is used for distance prediction, replacing the ranging measurement and being used for consensus extended Kalman filter (CEKF) measurement update to reduce the degree of relative navigation error divergence. Numerical simulation was conducted with three UAVs performing cooperative flight, the results show that the proposed algorithm can provide reliable distance prediction during ranging outage and effectively reduce the divergence of relative state estimation error.
Features for a Style for Push-communication Integrated Rich Web-based Applications

The development aspects of rich web-based applications have evolved; however, abstract concepts, like styles and patterns, are still lacking. If an abstract style for rich web-based applications is available, it can support the whole engineering process in many ways, like assisting in designing aspects and the system’s evolution. We have produced an abstract architectural style named RiWAArch style for standard rich web-based applications, and we are working on extending the same to realize integrating push-communication. Push-communication has become a contemporary requirement in developing features like real-time notifications in rich web-based applications. However, the features to be expected from a style to realize the integration of the push-communication are not yet recognized. This concept paper proposes a set of features to be expected from a style for push-communication-integrated rich web-based applications. Our ongoing research will later utilize these features to form requirements and design a comprehensive style by extending the RiWAArch style to realize the abstract features of integrating true push-communication into rich web-based applications.
Detection of Cyberbullying Tweets in Twitter Media Using Random Forest Classification

Users of social media platforms have become more vulnerable to violent crimes, cyberbullying, and hate speech as a direct result of their increased use of online platforms, which has also contributed to this vulnerability. Another factor that has contributed to this vulnerability is the fact that users of online platforms have increased their usage of these platforms. It is of the utmost importance for the prevention of cyberbullying (CB) in smart cities to design a self-sufficient anti-cyberbullying engine that can recognise cyberbullying texts in social media posts. This is one of the most important steps that can be taken. Those who live in smart cities won’t have to stress about the possibility of getting into altercations with their neighbours because of this, which will allow them to unwind and take more joy in life. In this paper, a state-of-the-art automatic classification model that can recognise CB texts without requiring them to be constrained into a specific shape in high-dimensional space. This model was conceived of and created. Due of these constraints, we designed a text classification engine that first pre-processes the tweets by removing noise and other background information. Then, it classifies the data without overfitting the data by extracting the necessary features and then classifying the data. The random forest classifier can obtain a higher level of accurate classification than other types of classifiers, such as the conventional classifiers. The findings of the validation indicate that the random forest classifier, which has increased text classification accuracy, gives accurate classification results.