Reviewerly/exHarmony huggingface dataset:
Postindustrial Society and Global Informational Space as Infrastructure Medium and Factor for Actualization of the State Informational Security

Global information integration of mankind in the vast majority of spheres and areas of its life beyond any political, economic, racial, and cultural differences is an integral part of globalization. At the current stage, such socio-humanitarian phenomenon led to the creation of single global information space. Characteristic features of the information society are revealed. Level of stability and protection of political, socioeconomic, military defense, spiritual-cultural, and other spheres of society from each of the six identified groups of dangerous, destabilizing negative destructive threats is characterized by the information security in the global information space of society. All these harm national interests of the state, sustainable development of society, well-being, and health of every citizen. It is substantiated that successful entry of states into the global information space presupposes orientation of the state information policy, clearly formulated in conceptual documents in terms of its strategic goals and formation of new information culture of society.
A New Declassification Method for Vector Geographic Data

With the acceleration of urbanization, society has entered an era of data explosion. It is difficult to balance confidentiality and sharing in the application of vector geographic data. Based on this, this paper proposes a method for declassifying vector geographic data which balances confidentiality and sharing. Firstly, a declassification model is constructed to ensure the sharing of vector geographic data, in other words, the declassification of classified data. Secondly, digital fingerprint technology is used to enable copyright protection of declassified data. Finally, verify whether the proposed method can give consideration to both confidentiality and sharing after declassifying vector geographic data. The experiments show: The proposed method can declassify the geometric precision of vector geographic data, and the declassified data can maintain a good graphical form and spatial relationship with good usability. And a reference for the balance between confidentiality and sharing is provided, which safeguards the safe use of vector geographic data in the urbanization process.
A Multi-Candidate Batch Mode Active Learning Approach

Active learning (AL) is a popular method for addressing the problem of high labeling during sample training. Its main issue is determining how to establish a sample selection method. Existing research methodologies combine sample diversity and uncertainty, but they only study one candidate sample set, making it difficult to weigh the proportion of the two types of tactics in different circumstances. Therefore, this study proposes a batch-mode active learning method based on the multi-candidate-set that incorporates the screening findings of multiple pre-candidate sets while improving strategy stability. To efficiently screen out representative samples, a K-center screening strategy is adopted. On different image datasets, this method outperforms the baseline strategies and is more stable than the single candidate set strategy.
Multi-agent logic for reasoning about duties and powers in private law

Duties and powers are two fundamental notions in private law. In this work, we provide our conception of duties and powers and present a logic for reasoning about them. We treat duties as agents' obligations towards others to perform actions. We think that powers are agents' legal abilities, conferred by law, to change legal positions between agents. How to exercise powers is also specified by law. Many factors, including the exercise of powers, fulfillment of duties, violation of duties, and factual changes in the world, can change duties. The ontic level of the logic is a multi-agent dynamic logic, where agents have abilities to change atomic facts. At its normative level, agents have duties towards others to change atomic facts, and have powers to change duties by changing atomic facts. When agents behave, the ontic and normative aspects of the world change accordingly. The implications of the formalization are studied extensively.
A Global Correction Framework for Camera Registration in Video See-Through Augmented Reality Systems

Abstract Augmented reality (AR) enhances the user’s perception of the real environment by superimposing virtual images generated by computers. These virtual images provide additional visual information that complements the real-world view. AR systems are rapidly gaining popularity in various manufacturing fields such as training, maintenance, assembly, and robot programming. In some AR applications, it is crucial for the invisible virtual environment to be precisely aligned with the physical environment to ensure that human users can accurately perceive the virtual augmentation in conjunction with their real surroundings. The process of achieving this accurate alignment is known as calibration. During some robotics applications using AR, we observed instances of misalignment in the visual representation within the designated workspace. This misalignment can potentially impact the accuracy of the robot’s operations during the task. Based on the previous research on AR-assisted robot programming systems, this work investigates the sources of misalignment errors and presents a simple and efficient calibration procedure to reduce the misalignment accuracy in general video see-through AR systems. To accurately superimpose virtual information onto the real environment, it is necessary to identify the sources and propagation of errors. In this work, we outline the linear transformation and projection of each point from the virtual world space to the virtual screen coordinates. An offline calibration method is introduced to determine the offset matrix from the head-mounted display (HMD) to the camera, and experiments are conducted to validate the improvement achieved through the calibration process.
Automated Integration of Heteregeneous Architecture Information into a Unified Model

As software systems are increasingly complex, architecture documentation becomes more important. Initial documentation of a system’s architecture needs to be kept up-to-date as the system evolves. Therefore, automated support for maintaining and evolving architecture information and documentation of interconnected and heterogeneous systems is highly beneficial to engineers, architects, and other stakeholders. To achieve this, we propose to automatically integrate recovered architecture information from heterogeneous data sources and architectural artifacts into a unified data model to create integrated views. Integrated views provide a holistic and up-to-date system representation. In this work, we present an integration approach for architecture information in a unified data model that serves as a digital architecture twin representing the current architecture of a system. We show that the integration approach successfully integrates architecture information by applying adapted metrics.
High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

Common line-structured light center extraction algorithms have limitations in accuracy and robustness when extracting the center of line-structured light obtained from objects with complex surface structures. In this study, we propose a high-precision center extraction algorithm for multi-curvature line-structured light. This algorithm improves the enhanced parallel thinning algorithm to eliminate redundant points and burrs and fills for missing center points at the end of the light stripe via the improved internal propulsion center extraction algorithm. Finally, sub-pixel centers are recalculated in the direction of the normal vector. A comparative experiment using three classical algorithms was conducted based on a tooth model with a complex curved surface. The experimental results show that the mean absolute error of this algorithm is <0.1, which is only half that of the best-performing dual-threshold grayscale center-of-gravity method in the classical method. Extraction of the center can be performed for multi-curvature line-structured light over 20 curvatures in the range of ( 0.0001 , 4.1439 ) / pixel − 1; moreover, the end of the light stripe and the identification of the truncation can be improved. Therefore, the proposed algorithm is suitable for contour detection of multi-curvature targets and effectively improves the accuracy of high-precision object detection.
A Robust Framework Based on Mutual Information and Contrastive Learning for Multimodal Violence Detection in Videos

With the development of modern information, the security of online videos content is receiving more and more attention.Previous work often ignores the correlation between different modalities. In order to solve the above problem, the task of violence detection is proposed to identify and locate violent frames in videos. In this paper, we propose a robust framework based on mutual information and contrastive learning for multimodal violence detection in videos. We use mutual information maximization to improve the correlation between audio and visual modalities. Meanwhile, we leverage contrastive learning to control modality fusion to capture modality invariance. We propose a robust framework consisting of three modules: 1) utilize a graph convolutional neural network module to capture global relationships across multiple video frames; 2) demonstrate a mutual information module to strengthen the correlation between audio and visual modalities ; 3) utilize the contrastive learning module to capture the modality invariance to facilitate modality fusion. For comparison with previous studies, we conduct extensive experiments on a public XD-Violence dataset. Experimental results show that our method achieves state-of-the-art performance.
A Study on the Discussion Method of Classics Reading : Based on 〈Masterpiece Reading〉 learners’ perception analysis

This study aims to investigate discussion methods applicable to classical reading courses based on learner perception analysis. In recent years, the rapid spread of artificial intelligence has increased the demand for human thinking and creativity. Discussion is a teaching method that can effectively strengthen these abilities, and it is necessary to strengthen research on specific applications in university education in the future. Therefore, this study examined whether there is a difference in students' perceptions of the &lt;Masterpiece Reading&gt; class and the improvement of core competencies according to the difference in discussion methods. The results showed that students who held group discussions had higher mean scores regarding lesson design, classroom activities, feedback and communication, achievement, and satisfaction as opposed to students who only held bulletin board discussions. In addition, both groups were most positive about improving their articulation competencies, while students who participated in bulletin board discussions were more positive about improving their self-management competencies. Also, students who participated in group discussions were more positive about improving their dedication and universal value competencies. We hope that this study will be helpful in designing and implementing discussion methods in future classical reading courses.
Design of Temperature Monitoring System for Air Cooled Radiators Based on LoRa

Air cooling technology is widely used in the north. When the winter temperature in the north is too low, the cooling radiator is prone to freezing, leading to the inability of air cooling units to operate normally and causing huge economic losses. In response to the above issues, this article designs a monitoring system for air cooled radiators based on LORA communication, based on Internet of Things technology. Analyze the A-type tower structure and heat dissipation principle of the air-cooled condenser, reasonably select the temperature measurement point position, and achieve temperature data transmission through LoRa communication and Ethernet communication technology. The monitoring software is programmed in C # language, which can visually display the temperature distribution information of the air-cooled condenser and store data in real time. The experimental results indicate that the system has certain guiding significance for preventing the freezing phenomenon of air cooled condensers and optimizing operation.
Study on the Relationship Between Emotional Rhythm and Children's Emotional Experience

"Owl moon" is an emotionally and artistically compelling picture book about a little girl who sees an owl at night. The purpose of this paper is to explore the performance and role of emotional rhythm in the picture book "Owl moon" from the perspective of emotional rhythm, and the significance of emotional rhythm in the creation and education of picture books. Using the literature research method and the example analysis method, this paper reveals the concrete expression of emotional rhythm and its influence on readers by analyzing the text, images, colors, and other elements in the picture book.
KnowLang – A Formal Specification Model for Self-adaptive Systems

KnowLang is a framework for knowledge representation and reasoning (KR &R) that aims at efficient and comprehensive knowledge structuring and awareness based on logical and statistical reasoning. It tackles both explicit representation of domain concepts and relationships and explicit representation of particular and general factual knowledge, in terms of predicates, names, connectives, quantifiers and identity. Moreover, it handles uncertain knowledge in which additive probabilities are used to represent degrees of belief. Other remarkable features are related to knowledge cleaning and knowledge representation for autonomic self-adaptive behaviour. Knowledge specified with KnowLang takes the form of a Knowledge Base (KB) that outlines a KR context. A special KnowLang Reasoner operates in this context to allow for knowledge querying and update. In addition, the reasoner can infer special self-adaptive behaviour. At its very core, KnowLang is a formal specification language providing a comprehensive specification model aiming at addressing the knowledge representation problem of self-adaptive systems. The complexity of the problem necessitated the use of a specification model where knowledge can be presented at different levels of abstraction and grouped by following both hierarchical and functional patterns. In this paper, we outline the formal semantics of the KnowLang multi-tier specification model. The model is outlined in terms of layers dedicated to knowledge corpuses, KB operators, and inference primitives.
Research on the Integration of Cloud Computing and Education Resource Sharing Scheme

Through the study of cloud computing technology, we analyze the significance of the integration of cloud computing and education resource sharing, and design a four-layer model architecture for the integration of cloud computing and education resource sharing platform based on the three-layer architecture of cloud computing, and elaborate the specific implementation functions of its resource layer, platform layer, application layer and login access layer, respectively, to deeply integrate cloud computing and education resource sharing.
Applications of Equivalence Relations in Mathematics

To help teaching of equivalence relations in discrete mathematics course, typical applications of equivalence relations in mathematics are explored fully and clarified elaborately. A variety of applications are grouped into three types, including viewing apparently different objects as the same one, grouping different objects into one class, and defining new mathematical objects, and each type is exemplified by specific instances. The emphasis is placed on intuition, roadmap and rigor, rather than technical details.
The Influence of Digital Teaching Process on College Students' Learning Performance

With the popularization and application of computer network in all kinds of education, digital learning has gradually become an important learning mode for college students in all kinds of schools in China. Based on this, this study attempts to investigate the influence of digital teaching process on college students' learning performance. The survey results show that the realization of digital teaching environment has changed the traditional relationship between teachers and college students. Compared with traditional teaching, the use of digital resources in teaching can stimulate college students' interest and improve their concentration time. Through the guidance of teachers, college students' two kinds of attention can be coordinated and alternated, so as to improve their attention to knowledge. College students who adopt digital teaching rate the tutors, and the results show that learners are very satisfied with the tutors' guidance. The realization of digital teaching environment enlarges the scope of college students' knowledge acquisition, and college students can easily acquire all kinds of knowledge they need through the network, and store, process and share it.
The enlightenment of western university construction to college education construction in China

There are many factors involved in improving the quality of university education.The most important factor restricting. the quality of university education is specialty construction.The university's specialty is the part that is most closely related to the labor market.Strengthening specialty construction,especially the construction of characteristic specialties is undoubtedly an important measure. A stone from another mountain can conquer a jade.The history and present situation of characteristic subject development and specialty construction in western universities enlighten us a lot.Under the realistic background of our country, the government and universities should learn from the concept of specialty management in western countries, and make joint efforts to strengthen the construction of characteristic specialty.
Graph Database Watermarking Using Pseudo-Nodes

Watermarking is used as proof of ownership for various data types such as images, videos, software, machine learning models, and databases. Datasets are crucial for data driven decision making using Machine Learning for tasks like prediction, recommendation, classification, and anomaly detection. Hence, it is not surprising that entire databases are being sold in data marketplaces. Protecting ownership rights upon such databases is, therefore, becoming increasingly important. Watermarking for relational databases has been an active field of research since 2002. However, how to watermark non-relational databases involving complex data types has largely remained understudied. In this paper we revise previously proposed techniques for non-relational database watermarking and introduce an improved technique for graph database watermarking inspired by Zhuang et al. [28]. Our technique employs randomization to generate a watermark in an efficient manner that avoids the computational complex genetic algorithm optimization of Zhuang et al. We evaluated our technique in terms of performance, usability, security, and robustness by implementing it as a proof-of-concept. Our results showed that our technique is efficient, secure and robust against guessing and deletion attacks.
Smart Parking System Using Heuristic Optimization for Autonomous Transportation Robots in Agriculture

A typical commercial berry farm is spread across quite a few hectares and has over hundreds of rows and tens of pickers operating on a daily basis. The berries in these farms are grown in a poly-tunnel parallel row based environment. This work develops a heuristic assignment technique called Smart Parking. Smart Parking is used for allocating parking spaces to autonomous transportation robots that assist fruit pickers working in such farms. The Smart Parking algorithm developed is put to test in a simulation software. It is also integrated with a robot controller called RASberry, which is a cutting-edge research project for running an entirely automated strawberry farm. The robots utilised for autonomous transportation in the RASberry project and the real-world trials in this study are Thorvald by Saga Robotics. To monitor and evaluate the effectiveness of Smart Parking, a number of real-world tests are carried out using the RASberry-Thorvald system. Smart Parking performs better than Standard Parking in terms of mechanical conservation and task completion time, as shown by graphical trend lines and statistical tests.
Quasi‐identifier recognition with echo chamber optimization‐based anonymization for privacy preservation of cloud storage

Summary The publication of data raises several security concerns, implying that when a reputable company offers data to a third party, personal information is not needed to be revealed. The fundamental issue that led to identity leakage is the connection of quasi‐identifiers (QIDs); however, the majority of researchers overlook the identification of accurate QIDs. This study developed a privacy preservation and clustering‐based quasi‐identification model based on the Echo Chamber optimization with a Z‐mixture parameter for privacy‐preserving combined data publishing to maintain the privacy of the data. The Echo chamber optimization is used to conduct a clustering‐based quasi‐identification to find the significant attributes especially, in determining the dimension of the solutions as well as the precise QIDs using the re‐identification risk rate as the fitness function. The loss of information is greatly reduced in terms of the metrics such as normalized certainty penalty metric, average equivalent class size metric, and discernibility metric. The developed optimized clustering‐based algorithm with the privacy preservation model extensively minimizes the leakage of private information and the utilization of data is well‐maintained compared with other existing algorithms.
Study on the Professional Construction Path and Mechanism of Teaching Management Personnel in University Education Management

In the realm of university education management, the professional development of teaching management personnel holds significant importance. These individuals bear the responsibility of guiding and overseeing educational and teaching endeavors, which directly impacts the quality of education and student development. However, several challenges and issues persist, including a lack of professional knowledge and skills among teaching administrators, insufficient practical experience, and limited application of innovative educational methods involving science and technology. Therefore, it becomes imperative to explore the path and mechanism for the professional development of teaching management personnel in university education management. This paper aims to discuss the professional construction path for teaching management personnel, encompassing aspects such as learning and training mechanisms, accumulation of practical experience, and continuous self-development. Additionally, the paper will delve into the mechanism of professional development, including the establishment of professional positions and promotion channels, as well as the implementation of evaluation and reward systems.
Application of Artificial Intelligence in Preschool Education Management

With the continuous development of artificial intelligence technology, its application in various fields has gradually expanded, including preschool education management. This paper aims to explore the application of artificial intelligence in preschool education management, focusing on its role in the optimization of educational resources, personalized teaching, student behavior analysis, etc. By analyzing the existing research results, this article will explore the advantages and challenges of artificial intelligence in preschool education management, and look forward to the future development trend.