Reviewerly/exHarmony huggingface dataset:
Application of Wireless Network Based on Artificial Intelligence in Network Teaching of Preschool Education Manual and Aesthetic Education Practical Course

For preschool education majors, art and crafts are required courses. This course is a hands-on exercise that helps students develop their creative awareness, design thinking, and hands-on skills. At the same time, it provides learners with the teaching thinking mode of art and handicraft theme and the form of online teaching helps learners to construct the thinking mode of online learning. As a new teaching form, online teaching is gradually leading the reform in the field of education. However, the majority of present online teaching is simply a continuation of traditional teaching thought, relying on substantial teaching research rather than utilizing its particular advantages to develop and expand. Also, the connotation depth of traditional teaching may not involve the essential problem of teaching application itself—teaching methods and experience of acquiring knowledge. As a result, determining how to characterize an individual’s ability to acquire knowledge and successfully assessing the level of growth of an individual’s cognitive structure is critical to the development of a new generation of online education. In order to prepare for the next generation of network teaching, this article uses artificial intelligence and wireless networks to teach preschool education manual and aesthetic education practical courses through the Internet. From the perspectives of individuals and groups, two teaching case recommendation experiments are given. The experiment’s end result confirms the practicality and logic of the evaluation model described in this research, which is based on individual cognitive structure.
Stages in Theory and Experiment. Fuzzy-Structuralism and Piagetian Stages

Abstract The theory of stages in cognitive development is one of Jean Piaget’s enduring legacies, but it has also borne the brunt of much criticism. It maintains that intelligence develops in an invariant sequence of stages, and, in this paper, I situate Piaget’s conceptions of stages historically and functionally in the context of genetic epistemology, his research programme. I highlight some of the objections raised, and I show how the disparity between the conceptions of theoretical and empirical stages in Piaget’s theory is commensurate with the fuzzy-structuralist model of the relationship between theory and empirical research conceived by Rudolf Seising on the basis of Lofti A. Zadeh’s fuzzy set theory. Further, I propose a fuzzy conception of the notion ‘stage’, which not only captures its ordinary use in fuzzy space between theory and empirical research but also does justice to both the construct validity and quantitative variability of stages in empirical research. I therefore open a fuzzy-structuralist perspective on the Crisis of Variability afflicting Piaget’s stage theory during the 1970s and conclude retrospectively that the rift it caused was not necessary since the invariance and variability of stages is not irreconcilable.
MASTIFF

The Minimum Spanning Forest (MSF) problem finds usage in many different applications. While theoretical analysis shows that linear-time solutions exist, in practice, parallel MSF algorithms remain computationally demanding due to the continuously increasing size of data sets.
Research on the Construction of English Autonomous Learning Model Based on Computer Network-Assisted Instruction

As a supplement to traditional teaching methods, computer-assisted teaching methods can reflect modern educational concepts, such as creating student-led, teacher-led environments. The goal of college English education is to enable them to communicate effectively in English in their future academic, work, and social interactions, while also developing students' self-learning skills. Chinese society improves overall cultural competence and adapts to the needs of international communication. Self-directed learning is not static and will increase or decrease with time, discipline, and conditions and is an evolving process. Understanding learning, taking responsibility for one's own learning, and learning how to learn are all beneficial. Students abound in school life and even throughout their lives. In this paper, we try to propose a computer-based method for constructing an independent English learning model based on a practical study of computer network technology for the development of self-learning ability of non-English majors in a university. This paper uses comparative analysis techniques to compare traditional paper-and-pencil examinations and computer-based online evaluations and analyzes the effects of each. The survey showed that 81% of the students preferred the computer-based assessment. Therefore, the focus of this research is to strengthen the oral English training in college and create an authentic English learning environment for students to really feel the standard English pronunciation, intonation, and knowledge of grammar, listening, reading, writing, and translation.
A Flexible Interpreter For Intent Realisation

Today we see more and more systems support intent languages, frameworks and interfaces. The incorporation of these approaches provide elements of abstraction between the request and the execution. However mapping is still required to get from the abstract to the real. This mapping often introduces elements of rigidity into the system. This work presents a flexible approach to intent driven systems through an interpreter for intent realization. The mechanism of the interpreter is demonstrated through interactions with mock slice manager functions running on an open source, functions as a service platform. The interpreter accepts intent messages containing a request in the form of an English sentence, along with required parameters for the request. This information is then used to identify the appropriate functions available to the interpreter. These functions are described in the Functionality Template model. Once identified, the Functionality Template guides the building of an action in response to the intent message.
Versatility or validity: A comprehensive review on simulation of Datacenters powered by Renewable Energy mix

Nowadays, DataCenters powered by Renewable Energy mix (DCRE) appear worldwide. It brings a hot topic on studying how to fully utilize renewable energy and save brown energy. However, for the vast and complex infrastructure, researchers would perform their studies on a DCRE simulator rather than a real-world DCRE. It brings a question: do these simulators have the same functions and result for the same optimization approach? This paper focuses on the question and proposes the versatility and validity models and relationships for these simulators. We use the term ”simulators” broadly, encompassing any general-purpose simulation tools, experiment-purpose simulation approaches, and other simulation works on DCREs. We have reviewed the simulation works for DCRE in the last ten years. After that, we rebut the widespread practice that a simulator has functions for the particular study with an assumed validity. Furthermore, we claim that uniform metrics for both versatility and validity lead to a better simulator. Finally, we back up this claim with comprehensive benchmarking. The experimental results show that the more versatile a simulator is, the better its validity. • We review the simulation works for DCRE in the last decade. • Based on the simulation results, we claim that the simulators’ functions vary. • We propose the versatility and validity metrics for evaluating the simulators. • We prove the win-win relationships between two metrics with comprehensive benchmarking.
On Commemorating Hrant Dink: Affective Nationalism, Hate Speech, and Digital News Media Users

This article focuses on the digital reproduction of hate speech. It investigates the normalization of affective nationalistic discourses via user comments posted on digital news media websites. I analyze digital posts concerning the murder in Turkey of Hrant Dink, a well-known Turkish Armenian journalist, on the websites of the two most popular Turkish newspapers. This examination considers readers’ comments on the day of the murder, January 19, 2007, and the annual commemorations over the subsequent 6 years. Based on my findings, I argue that nationalistic rhetoric is normalized via six discourses of othering: militarization, identity, us versus them, blaming, rejection, and indifference. Furthermore, I demonstrate how users repurpose online comment sections to reproduce notions of inferential racism by producing affective nationalism vis-à-vis Armenian–Turkish relations.
A Critical Analysis of the Blockchain Technology and its Applications

Artificial intelligence is a branch of computer science and further machine learning is a subpart of artificial intelligence. Machine learning (ML) is a sort of artificial intelligence (AI) that enables software programs to improve their prediction accuracy without being expressly designed to do so. Machine learning is significant because it enables businesses to see changes in customer behavior and company operating practices while also assisting in the creation of new goods. In recent years, blockchain technology has become increasingly popular. Individuals can interact directly with each other through a highly secure and decentralized system, without the need for a middleman, thanks to this technology. Machine learning, in addition to its strengths, may assist in overcoming many of the restrictions that blockchain-based systems face. These two technologies when combined can produce high-performing and valuable solutions. In this manuscript, blockchain technology and how machine learning skills can be combined with a blockchain-based system are studied. Moreover, a few of the most common applications and use cases for this integrated strategy are discussed.
Application of Blockchain Technology in Healthcare: A Systematic Review

In the last few years, blockchain technology has found its feet in this era of various technologies. Finance, government, energy, health, etc. fields are showing significant interests in blockchain technology. This research paper delivers a thorough study of blockchain 's usefulness and effectiveness in this emerging domain of health care. The continuous research with blockchain technology in this domain is arising very rapidly and researchers are willing to go after this. We have identified a number of use cases in this field using blockchain technology, a few of them are - remote patient monitoring, sharing electronic medical records, drug purchases, pharmaceutical supply chain and many others. Additionally, we focused on figuring out the constraints of the study approaches and examining a few research problems and subject for furthermore research.
The Role of Digital Transformation in Improving the Judicial System in the Egyptian Council of State

The purpose of this paper is to present the meaning of e-justice, the most important advantages of the e-justice system, and the challenges that face its application, the system in the Egyptian economic courts, and how can the challenges of the e-justice system be overcome from the reality of international experiences. The methodology of this paper depends on mainly two methods: the descriptive-analytical method and the analysis approach. Moreover, the paper reached the most important result that the judicial system generally can be improved by using an e-justice system, particularly in the Egyptian Council of State.
Application of Substring Search Algorithms to Hindi based on Pattern Recognition

Substring search is a basic operation in all text processing applications. Among many algorithms for this purpose, there are three that are very common, namely Knuth-Morris-Pratt, Boyer-Moore and Rabin-Karp algorithms. All these are applied generally to English (byte-based text). This paper presents the experimental validation of these algorithms when they are applied to Hindi (UTF-8 encoded) text made up from 49000 unique Hindi words.
Optimizing Cybersecurity Investments over Time

In the context of growing vulnerabilities, cyber-risk management cannot rely on a one-off approach, instead calling for a continuous re-assessment of the risk and adaptation of risk management strategies. Under the mixed investment–insurance approach, where both risk mitigation and risk transfer are employed, the adaptation implies the re-computation of the optimal amount to invest in security over time. In this paper, we deal with the problem of computing the optimal balance between investment and insurance payments to achieve the minimum overall security expense when the vulnerability grows over time according to a logistic function, adopting a greedy approach, where strategy adaptation is carried out periodically at each investment epoch. We consider three liability degrees, from full liability to partial liability with deductibles. We find that insurance represents by far the dominant component in the mix and may be relied on as a single protection tool when the vulnerability is very low.
Evaluation of Japanese Teaching Quality Based on Deep Neural Network

The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors’ teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor’s weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers’ teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers’ teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation’s dependability and makes the outcomes more effective and objective.
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data

Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In this work, we show that state-of-the-art SSL algorithms suffer a degradation in performance in the presence of unlabeled auxiliary data that does not necessarily possess the same class distribution as the labeled set. We term this problem as Auxiliary-SSL and propose AuxMix, an algorithm that leverages self-supervised learning tasks to learn generic features in order to mask auxiliary data that are not semantically similar to the labeled set. We also propose to regularize learning by maximizing the predicted entropy for dissimilar auxiliary samples. We show an improvement of 5% over existing baselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeled samples and all unlabeled data is drawn from the Tiny-Imagenet dataset. We report competitive results on several datasets and conduct ablation studies.
Analysis of Digital Photography Technology in the Era of Big Data

The term “Digital Photography” has been a relatively new research topic in the field of computer-mediated technology. Although Digital Photography emerged thirty years ago, continuous developments and upgradations are being recorded frequently. Advanced technologies like Digital Technology transformed all the technology into digital mode. The field of photography underwent several developments in terms of camera quality, image quality, image clarity, image transfer, image storage, and other sensitive surfaces. Most of the studies confirmed that digital photography permitted the photographer to record videos at high resolutions. All the developments in the field of Digital Photography have largely contributed to the concept of modern photography and enabled the photographers to produce high-quality images. It also helped photographers enhance their photography skills and accomplish their tasks. All the transformations in the field of Digital Technology happened at a pace, and it was extremely difficult. Therefore, the current paper sheds light on the developmental stages of digital photography. It also highlights the significance of digital photography technology in the era of big data.
G2Auth

Because of its cost effectiveness and timeliness, package delivery using unmanned aerial vehicles (UAVs), called drone delivery, is drawing growing attention. Authentication is critical for ensuring that a package is not picked up by an attacker's drone or delivered to an attacker. As delivery drones are costly and may carry sensitive or expensive packages, a drone should not get very close to a person unless she is authenticated; thus, conventional authentication approaches that require human-drone physical contact do not work. Existing authentication methods for drone delivery suffer from one or multiple of the following limitations: (1) requiring special user-side hardware; (2) enforcing one-way authentication only; (3) being vulnerable to relay attacks; (4) having compatibility issues. We present the first system, named Greet-to-Auth (G2Auth, for short), that supports mutual authentication between a user and a drone, without these limitations. A user waves her hand holding a smartphone to conduct the authentication. The evaluation shows that it is secure, accurate, usable, and robust.
Survey of the Security Algorithms over Cloud Environment to Protect Information

Well suited data protection has emerged as a critical trouble for data generation. In cloud it is in particular essential due to the fact the information is saved in one of a kind place around the cloud arena. Records safety and protection are two significant aspects of user attention in cloud era, although various Techniques-algorithms have been evolved in unique cloud environments like banking, schooling, real market and many others. Information safety and privacy safety troubles are critical to our challenge for every computer and software within the cloud structure. This paper is the studies of simple cloud structure and protection troubles in cloud; additionally taken a look at various cryptography algorithms for data protection and displaying the overall performance among them over cloud platform. Presented study shows the significance of the security algorithm that can be used to enhance data protection.
Study on Deep Reinforcement Learning for Mobile Robots Flocking Control in Certainty Situations

Mobile robots are the widely used machines that will be a part of life. It can easily be scaled to meet the requirements of human uses and can move to different target areas freely. However, mobile robots have a problem with mobility, which needs to respond to human activities and obstacles. Reinforcement learning (RT) is a part of machine learning, which enables the machine to learn by themselves. It can develop in a real-world environment without machine teaching or patterns. This paper proposed the study on the flocking control simulation, which avoids the obstacles. Mobile robots in simulation presented by using Multi-Agent Deep Deterministic Policy Gradient (MAD-DPG). The proposed algorithm, the deep reinforcement learning algorithm, is the main navigation of robots, which find the target, maintain distance between robots, and avoid collision with obstacles by learning features and characteristics with certainty environment, obstacle, robots, and target. On the simulation, the authors operate all of the positions in the environment, and robots speed in the environment as the state.
Analysis of the Relationship between Transformational Leadership and Educational Management in Higher Education Based on Deep Learning

Leadership behavior has been emphasized as one of the most important influencing factors in the innovation process. Leaders can encourage subordinates to innovate by creating the right environment, promoting knowledge integration, and setting specific goals. However, different leadership styles make different decisions and behaviors in the innovation process, and the final innovation effect is also different. Today, in the context of China, most business leaders still adopt "paternalistic" or "authoritarian" leadership behaviors, but more and more entrepreneurs and scholars are aware of the importance of this leadership style in enhancing employee creativity. Authoritarian leaders are more likely to exercise more control and supervision over team members, limit the autonomy of team members, and reduce work initiative and creativity. Although the positive effect of transformational leadership on employee creativity has been recognized by some scholars, in the real work environment, this leadership style rarely appears, especially in the context of China. This study first constructs a theoretical model of how transformational leaders in colleges and universities affect educational management innovation through the atmosphere of school organizational innovation, based on the deep learning theory and other related research results, and then puts forward research hypotheses on this basis. Secondly, a measurement scale was designed according to the existing research results, and the scale was revised through the pretest to form the formal questionnaire of this study. This research uses cluster sampling and random sampling to conduct a questionnaire survey on 1022 college teachers and uses the SPSS20.0 and AMOS21.0 to conduct an empirical analysis on the survey data. Each measurement scale was tested by exploratory factor analysis and confirmatory factor analysis. The experimental results show that the transformational leadership style of college principals has a positive impact on teachers' teaching innovation. There is a positive correlation between the influence of charisma and teachers' teaching management, and there is a positive correlation between intellectual stimulation and teachers' teaching management.