Reviewerly/exHarmony huggingface dataset:
Blockchain-based Authentication Scheme for Vehicle Network Nodes

Internet of Vehicles (IoV) has developed into a useful service platform between vehicles and other vehicles, vehicles and roadways, and vehicles and pedestrians as a result of advancements in communication technology. However, along with the intelligent and networked IoV, IoV security issues are increasingly emerging, which often lead to extremely serious consequences. For example, when a vehicle is remotely attacked and maliciously controlled, it can lead to user privacy leakage. Attackers can also control the driving of vehicles through attacks, and there may even be a large number of connected cars being controlled in bulk, ultimately causing major social security incidents. In the Internet of Vehicles, authentication technology is one of the main solutions to address the security issues of the Internet of Vehicles. Many researchers have conducted some research on the authentication technology of vehicular network, but the traditional authentication scheme under the centralized system has problems such as frequent single-point failures and high overhead. When blockchain technology is properly implemented to the vehicle network environment, its security outperforms that of centralized systems due to features like decentralization, tamper-proof, security, and traceability. Because of this, we construct an optimization technique based on the Practical Byzantine Fault Tolerance (PBFT) algorithm in this work and present an efficient IoV decentralized authentication scheme based on the consensus algorithm of blockchain technology. After performance analysis, it is proved that our scheme has better performance and higher security.
Development of a Cognitive Model of the Interaction of Knowledge Carriers in the Educational Environment

Today the main trends in the development of advanced education are associated with the presence of competitive relations between universities. Thus, the main index of educational exertion is the quality of training of specialists with the necessary capabilities and meeting the conditions of the request. The research shows that capabilities and knowledge need to be continuously bettered. These changes are formed through the introductory parcels of scholars, brigades of preceptors, deep knowledge systems and learning systems due to the transfer of knowledge from outdoors to the educational terrain. The knowledge operation structure contains four abecedarian realities of knowledge carriers the client (students), a platoon of mentors, training systems and in-depth knowledge. These things are always engaging in knowledge. The results attained are the base for substantiating the conception of lifelong learning and fresh training of mentors in the event of a lack of knowledge necessary for the perpetration of a unique educational exertion.
Speech Synthesis with Image Recognition Using Application of CNN and RNN

In this paper, we are trying to make a portable image recognition machine that will read out the objects in the image in the form of speech with the help of CNN and RNN. This can be very helpful for recognizing different objects in real-time. We will be using Raspberry Pi, which itself is a portable computer to do all the work at hand. With the help of the Raspberry Pi camera, a real-time image will be captured just by showing the object to the camera, and then with the help of a Convolutional Neural Network (CNN), the image will be recognized. The CNN architecture has ResNet which has the capability to handle sophisticated deep learning tasks and models. The speech part will be constructed with the help of a Recurrent Neural Network (RNN). Recurrent Neural Networks architecture has internal memory that stores the state it has gone through i. e. the inputs it has taken this help and makes it a choice for a machine learning problem. Example of RNN includes Apple Siri and Google Voice Search. The speech will be the output in the form of a voice.
Finding the best strategy in 2-player games through iteration

Various games can have multiple outcomes, based on the decisions of the various stakeholders or players.In this paper, we created a program (in Python) that finds the type of dominant strategy in 2-player games, using the potential payoffs or utility returns for each of the possible moves a player plays.Specifically, the code was ideally made for a game where each of the players has 2 moves thus 4 total possible outcomes for each player with their own payoffs.We were able to process the inputted data for each of the 2 players through the use of a 2:2 matrix.By comparing payoffs or specific values in the matrix using logical steps and conditions, we were able to determine what move would be most beneficial and effective for each player, along with the type of dominance that move has.
Multi-stage reasoning on introspecting and revising bias for visual question answering

Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer predication, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning, and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental results show that our network achieves significant performance against the previous state-of-the-art methods.
Flexible-elastic deformation measurement of ZnS:Cu2+ mechanoluminescent film using visual inspection and digital image correlation

ZnS-based mechanoluminescent film has been widely used in the fields of stress visualization and stress sensing, due to its high brightness and repeatable stable luminescent characteristics.To evaluate the flexibleelastic deformation performance of ZnS-based mechanoluminescent film, both visual inspection and digital image correlation (DIC) are, respectively, employed for measuring the ZnS-based mechanoluminescent film.ZnS:Cu 2+ mechanoluminescent powders are first mixed with polydimethylsiloxane (PDMS) matrix to produce ZnS:Cu 2+ -PDMS mechanoluminescent film.Then, two measurement experiments are, respectively, conducted to investigate the mechanical response and the flexible-elastic deformation performance of the prepared ZnS:Cu 2+ -PDMS mechanoluminescent film.On one hand, the mechanical response performance of the ZnS:Cu 2+ -PDMS mechanoluminescent film is validated by visual monitoring of composite concrete fracture processes.On the other hand, the prepared ZnS:Cu 2+ -PDMS mechanoluminescent film is also measured by DIC to obtain its full-field deformations and strains information.The flexible-elastic deformation performance of the ZnS:Cu 2+ -PDMS mechanoluminescent film is well demonstrated by the DIC measured results.
Gravitational transport system, its principles of construction and management

The paper considers the principle of constructing a gravitational transport system designed for container transportation of the technological materials. It proposes a scheme of the transport system providing the container shuttle motion between the stations. A scheme is developed, and description of the stations operation principle is presented being a part of the transport system; the compressed air energy is used there to provide transport container with the potential energy ensuring its motion along the transport route under the gravity influence. The proposed station scheme makes it possible to consider the station as a functionally complete module forming the basis for possible development of the extended transport systems with various configurations. An algorithm for the station functioning was developed that implemented the processes of sending and receiving a container. A microprocessor system for managing the station mechanisms is proposed, which controls the sensor signals state and generates control signals for the compressed air distributors. A computer model of the control system was designed and developed to debug algorithms for controlling the transport system stations. The considered schemes and algorithm are intended to develop the technological transport lines.
DEMOCRATIZING EDUCATION: PEDAGOGICAL ACTIVISM AND TECHNOLOGICAL FUTURES

The current and ongoing urgencies for pedagogical invention in both philosophy and practice demand teaching and learning designs and applications that have consequence in terms of civic engagement.The achievement of democratic civic understandings, values, and practices depends on the recognition by educators that they are in fact, charged with the critical and ennobling task of developing through content and action the present and future civic capacities of their students; and such capacities are antidotal to the troubling ascendency of populism, political autocracies and thuggeries around the globe.In an era irrefutably afflicted with the profusion of disinformation, the erosion of public trust, and the destabilization of truth, education for democratic participation is both intervenor and instigator for the broad project of citizenship and social change.In this sense, pedagogies are necessarily activist in their commitment to social engagement and change.The recognition of the exigencies of democratic education is more than a matter of curricula content that engages critically with the definitions and principles of deliberative democracy; rather, such recognition should result in pedagogical approaches and practices that model democratic participation especially in terms of learning environments and an infinitely more expansive view of the classroom.Key and orienting in the project of democratizing education are the determinants and elaborations of technology, and in particular AI in the educational context.While there is considerable handwringing around potential compromises to academic integrity and a rapid and unrestrained increase in academic dishonesty without efforts to neutralize the foundations and capacities of AI-generated papers, this paper explores affordances of current and emerging technologies in terms of precise practices toward democratic education, from pedagogical innovation, creative approaches to course design, new evaluation methods and criteria, expansive and experiential learning spaces, and more.
THE RELATIONSHIP BETWEEN ENTREPRENEURIAL LEADERSHIP AND TEACHER JOB SATISFACTION: THE MEDIATION EFFECT OF INTRAORGANIZATIONAL FACTORS

Entrepreneurial leadership has emerged following new privatization and autonomy policies in Israel.As a result of these policies, principals are pressured to assume the role of entrepreneurial leaders to find outside resources for the funding of innovative projects and programs in school.Entrepreneurial leadership has been defined in terms of leader traits and behavior.We define entrepreneurial leadership as principals' proactiveness and school innovativeness.Proactiveness means the principals adopting an active search for new opportunities to shape the school environment; innovation is defined as the actual implementation of these opportunities in school.Entrepreneurial leaders need to take into consideration intra-organizational factors.Principals as entrepreneurs should ensure teachers' willing participation in implementation and overcome their natural resistance, because those changes require more time and create a heavier workload.Teachers' readiness to make the extra effort depends on their job satisfaction.The literature indicates that job satisfaction is related to teachers' professional development, teacher-student positive interaction teamwork, and teacher/student ratio.Therefore, we hypothesize that proactiveness and innovation will have a positive effect on teachers' job satisfaction; mediated by professional development, teacher-student interaction, teacher/student ratio in school, and teamwork.Our study is based on the responses of 410 Israeli teachers from all levels, teaching in Jewish and Arab State public schools, who filled out a questionnaire.Conducting a univariate GLM model, the results indicate the following: All levels (low, medium, high) of school innovativeness and principals' proactiveness have a positive effect on teachers' job satisfaction.In addition, when the mediating variables of professional development, teacher-student interaction, teamwork, and T/S ratio are introduced, they have a significant effect on job satisfaction.Furthermore, the appearance of the mediating variables weakens the effect of proactiveness and innovativeness on job satisfaction.Our study contributes to the knowledge on the impact of entrepreneurial leadership on teachers' outcomes.This has implications on programs of principal training.
GAMIFYING CYBERSECURITY: A STUDY OF THE EFFECTIVENESS OF A SPECIFIC GAMIFIED TOOL

Secure software development refers to the practice of creating software applications and systems with a focus on security.Traditional approaches to teaching secure software development often involve classroom lectures and laboratory assignments that roughly simulate real-world scenarios.However, such approaches may not always be engaging or effective for students, who may struggle to connect the abstract concepts of secure software development to practical, real-world applications.Gamified learning refers to the integration of game elements and mechanics into the learning process to enhance engagement, motivation, and retention of knowledge or skill and can offer students a more interactive and immersive learning experience.In this study, we explored the use of Hack The Box (HTB), a gamified platform for learning cybersecurity, as a tool for teaching secure software development to undergraduate students in a software engineering program.Students were given access to the platform and asked to complete a set of challenges designed to reinforce key concepts, such as secure coding practices, vulnerability assessment, and incident response.Students were also asked to complete a set of questionnaires to gather data on their attitudes towards the traditional laboratory approach versus the gamified approach using HTB.The results of the study indicate that the use of gamified platforms such as HTB can be an effective tool for teaching secure software development.Students reported feeling more engaged and confident in their ability to apply secure software development practices after using the platform.They also found the platform to be more engaging and challenging than the traditional laboratory approach.Moreover, students who reported finding the traditional laboratory approach to be challenging or unengaging found HTB to be a valuable and effective alternative.The use of gamified platforms also has several other potential benefits.For example, it can provide students with immediate feedback on their progress and performance, which can help to motivate them and encourage them to persist through challenging problems.Additionally, gamified platforms can provide a more accessible and inclusive learning experience by accommodating different learning styles and levels of expertise.For instance, students who may not have prior experience with secure software development can benefit from the more interactive and hands-on approach provided by the platform.However, challenges such as the difficulty level of some challenges and the need for additional support materials need to be addressed to maximize its effectiveness.Overall, this study suggests that gamified platforms like Hack The Box can be a valuable tool for teaching secure software development to undergraduate students.Future research could explore the use of other gamified platforms and tools, and examine the potential benefits and challenges associated with their implementation.
Prediction of breast cancer using machine learning algorithms on different datasets

Breast cancer is a disease that is becoming more and more common day by day, causing emotional and behavioral reactions and having fatal consequences if not detected early. At this point, traditional methods are insufficient, especially in early diagnosis. In this context, this study aimed to predict breast cancer by using machine learning (ML) algorithms on different datasets and to demonstrate the applicability of these algorithms. Algorithm performances were compared on balanced and unbalanced datasets, taking into account the performance metrics obtained in applications on different datasets. In addition, a model based on the Borda Voting method was developed by including the results obtained from four different algorithms (NB, KNN, DT, and RF) in the process. The prediction values obtained from each algorithm were written in different columns on the same excel file and the most repetitive value was accepted as the final result value. The developed model was tested on real data consisting of 60 records and the results were analyzed. When the results were examined, it was seen that higher performance was obtained with the proposed RF model compared to similar studies in the literature. Finally, the prediction results obtained with the developed model revealed the applicability of ML algorithms in the diagnosis of breast cancer.
A Decision Tree and Logistic Regression Algorithm-Based Model for Predicting Crimes Committed by Health Insurance Fraudsters

As the world's medical systems have matured over time, there are still unscrupulous medical institutions and participants in society who use illegal means to defraud medical insurance funds. However, the complexity of the people committing fraud, the difficulty of accounting for the actual amount of fraud in the healthcare system, and the variety of frauds make it impossible for the public security authorities to effectively combat them. In this context, this project aims to use pandas and Apriori association rule algorithms for data pre-processing and feature mining, to extract fields with high correlation, to create models related to health insurance fraud clues, and to filter out potential fraud suspects through the analysis of health care consultation information, combined with public security perception data, and to combat them effectively. This project applies advanced information technology to police fraudulent medical insurance fund clues mining, which can promote the advancement of policing in healthcare while facilitating the integration of advanced information technology with traditional fields, while safeguarding the people's basic right to health and survival.
Development of an Immersive Virtual Reality System to Practice the Lumbar Puncture Manoeuvre

Training in medical practices requires materials, patients and a context that allows students to become familiar with the environment and tools. However, this is not always easy to achieve. Immersive virtual reality can be a suitable tool for moving from theoretical studies to practice in a real context. This paper describes the process of analysis, design and development of a virtual environment in which lumbar puncture can be performed. It is a project in development that combines medical research with the latest advances in extended reality to improve the training possibilities of medical students.
Advantage of the Key Relay Protocol Over Secure Network Coding

The key relay protocol (KRP) plays an important role in improving the performance and the security of quantum key distribution (QKD) networks. On the other hand, there is also an existing research field called secure network coding (SNC), which has similar goal and structure. We here analyze differences and similarities between KRPs in general and key relay using SNC schemes (KRPs-by-SNC) rigorously. We found, rather surprisingly, that there is a definite gap in security between KRPs in general and KRPs-by-SNC; that is, certain KRPs achieve better security than any SNC schemes on the same graph. We also found that this gap can be closed if we generalize the notion of SNC by adding authenticated public channels; that is, KRPs are equivalent to KRPs-by-SNC schemes augmented with authenticated public channels.
Introduction

In this chapter, the mechanism and modeling method of image degradation are briefly described first. Then the research status and development trend of regularization methods, nonlinear objective function algorithms, and adaptive estimation of the regularization parameter for image restoration are discussed in detail.
An NLP Approach to Understand the Top Ranked Higher Education Institutions’ Social Media Communication Strategy

In this paper we examine the use of social media as a marketing channel by Higher Education Institutions (HEI) and its impact on the institution's brand, attracting top professionals and students. HEIs are annually evaluated globally based on various success parameters to be published in rankings. Specifically, we analyze the Twitter publishing strategies of the selected HEIs, and we compare the results with their overall ranking positions. Our study shows that there are no significant differences between the well-known university rankings based on Kendall τ and RBO metrics. However, our data retrieval indicates a tendency for the top-ranked universities to adopt specific strategies, which are further emphasized when analyzing emotions and topics. Conversely, some universities have less prominent strategies that do not align with their ranking positions. This study provides insights into the role of social media in the marketing strategies of HEIs and its impact on their global rankings.
FOREIGN LANGUAGE SPEAKING ANXIETY ON THE 6TH SEMESTER STUDENTS OF ENGLISH EDUCATION DEPARTMENT

The aim of this research is to discover the existence, the average level, and the most common type of foreign language speaking anxiety on college students. The approach of this research is quantitative. The research is conducted at the English Education Department of Universitas Negeri Manado. The population of the respondents of this research is the 6th semester students of the English Education Department of Universitas Negeri Manado. The sample consists of 20 respondents of the 6th semester students chosen using the random sampling process. The research instrument that the writers use in this study is the Foreign Language Speaking Anxiety Scale (FLSAS). The finding of this research shows that all of the respondents have experienced symptoms of speaking anxiety and have an average level of 90,3 for speaking anxiety. It is also found that the cause for foreign language speaking anxiety on most of the students is test anxiety. The result of the research suggests that the 6th semester students of the English Education Department of Universitas Negeri Manado have speaking anxiety with an average level of Moderate, and the most common type of speaking anxiety was Test Anxiety.