Reviewerly/exHarmony huggingface dataset:
Exploring the influence of government social media on cybersecurity compliance: employee attitudes, motivation and behaviors

Purpose This study aims to investigate the impact of employees’ engagement in government social media (GSM) on their cybersecurity compliance attitude, protection motivation and protective behavior, thereby contributing to effective cybersecurity practices at organizations. Design/methodology/approach A quantitative cross-sectional field survey was conducted to collect primary data in big cities and large provinces in Vietnam. The final data set of 323 responses was analyzed using the partial least squares-structural equation modeling approach to interpret the results and test research hypotheses. Findings Engagement in GSM positively influences employees’ cybersecurity compliance attitude (ATT). Perceived threat vulnerability and response efficacy also contribute to a positive compliance attitude, although self-efficacy has a negative impact. Moreover, the cybersecurity compliance ATT significantly explains the information protection motivation, which in turn influences employee protective behaviors. However, the relationship between compliance attitude and protective behaviors is weak, unlike previous studies that found a strong correlation. Originality/value Although recent studies have explored specific information security practices in corporate and home contexts, the influence of GSM on individuals’ cybersecurity behaviors has received limited attention because of its novelty. This study contributes to the existing body of knowledge by investigating the impact of GSM on cybersecurity behaviors. This study provides significant contributions to understanding social media’s effects of social media on individuals’ cultivation processes, by expanding upon the protective motivation theory and cultivation theory. The results lead to practical suggestions for organizational managers and policymakers so that they can enhance their understanding of the importance of cybersecurity, encourage the implementation of self-defense strategies and highlight the significance of threat and coping evaluations in influencing attitudes and motivations.
The Relationship between Mastering Dictation and University Students' Achievement of Spelling Vocabulary and Writing Text

Dictation has been used in the field of language learningfor several years. However, most of the language instructors andmethodologists regard this method as useless and out-of-date. Indeeddictation is a teaching technique which enables English teachers tofocus on the language proficiency of their students . This paper explainswhat the teachers should do to help the students enhance enthusiasmand confidence for learning , stimulate learning motivation , reduce oreliminate their difficulty and take measures to improve their ownlanguage skills . This paper aims at investigating the relationshipbetween mastery of dictation and university students' achievement inspelling which in order to raise students' efficiency in vocabulary andtexts. This paper consists of three sections, the first as an introduction,and the second deals with literature review and the third is theconclusion.
On Emerging Methodology for Collection of Good Practices in the Area of Applied Artificial Intelligence

The work is fulfilled within the framework of Erasmus+ project "The Future is in Applied Artificial Intelligence" (FAAI) and devoted to the development the methodology for collecting and analyzing good practices in the field of applied artificial intelligence (AAI) regarding the competences, training, existing solutions and real cases, which can be used for developing training courses of competence based education. Here we propose the definition of good practice in the field of AAI together with the corresponding criteria and features. The offered methodology uses system research based on the data gathered from existing training courses in AAI, labor market, surveys filled in by academics, students and employers, AAI use cases in science and industry.
An Interpretation of E-HA^w inside HA^w

Higher Type Arithmetic (HA ω ) is a first-order many-sorted theory.It is a conservative extension of Heyting Arithmetic obtained by extending the syntax of terms to all of System T: the objects of interest here are the functionals of "higher types".While equality between natural numbers is specified by the axioms of Peano, how can equality between functionals be defined?From this question, different versions of HA ω arise, such as an extensional version (E-HA ω ) and an intentional version (I-HA ω ).In this work, we will see how the study of partial equivalence relations leads us to design a translation by parametricity from E-HA ω to HA ω .
Research and Analysis on the Labor Market in the Field of Applied AI

This article is fulfilled within the framework of Erasmus+ project "The Future is in Applied Artificial Intelligence (FAAI). It gives overview of current job market related to the field of Applied Artificial Intelligence. The data is obtained from online survey, and it gives highlights of several aspects of labor market divided into research and analysis of the market, and specific requirements necessary. Regarding research and analysis, the data provided deals with:-positions offered in the market.-machine learning problems occurring.-models being developed while resolving the real-world problem.-machine learning tasks to be solved.The collected data in the domain of job market requirements gives highlight about:-required programming languages.-educational requirements.-required competencies.Results given can serve as a guide to which competencies are necessary in the field of AAI and provide information for both professionals and curriculum creators.
Adoption of a COVID-19 Contact Tracing App by Czech Youth: Cross-Cultural Replication Study

During the worldwide COVID-19 pandemic crisis, the role of digital contact tracing (DCT) intensified. However, the uptake of this technology expectedly differed among age cohorts and national cultures. Various conceptual tools were introduced to strengthen DCT research from a theoretical perspective. However, little has been done to compare theory-supported findings across different cultural contexts and age cohorts.Building on the original study conducted in Belgium in April 2020 and theoretically underpinned by the Health Belief Model (HBM), this study attempted to confirm the predictors of DCT adoption in a cultural environment different from the original setting, that is, the Czech Republic. In addition, by using brief qualitative evidence, it aimed to shed light on the possible limitations of the HBM in the examined context and to propose certain extensions of the HBM.A Czech version of the original instrument was administered to a convenience sample of young (aged 18-29 y) Czech adults in November 2020. After filtering, 519 valid responses were obtained and included in the quantitative data analysis, which used structural equation modeling and followed the proposed structure of the relationships among the HBM constructs. Furthermore, a qualitative thematic analysis of the free-text answers was conducted to provide additional insights about the model's validity in the given context.The proposed measurement model exhibited less optimal fit (root mean square error of approximation=0.065, 90% CI 0.060-0.070) than in the original study (root mean square error of approximation=0.036, 90% CI 0.033-0.039). Nevertheless, perceived benefits and perceived barriers were confirmed as the main, statistically significant predictors of DCT uptake, consistent with the original study (β=.60, P<.001 and β=-.39; P<.001, respectively). Differently from the original study, self-efficacy was not a significant predictor in the strict statistical sense (β=.12; P=.003). In addition, qualitative analysis demonstrated that in the given cohort, perceived barriers was the most frequent theme (166/354, 46.9% of total codes). Under this category, psychological fears and concerns was a subtheme, notably diverging from the original operationalization of the perceived barriers construct. In a similar sense, a role for social influence in DCT uptake processes was suggested by some respondents (12/354, 1.7% of total codes). In summary, the quantitative and qualitative results indicated that the proposed quantitative model seemed to be of limited value in the examined context.Future studies should focus on reconceptualizing the 2 underperforming constructs (ie, perceived severity and cues to action) by considering the qualitative findings. This study also provided actionable insights for policy makers and app developers to mitigate DCT adoption issues in the event of a future pandemic caused by unknown viral agents.
Can Technology Initiate The Enhancement of Digital Literacy in Early Childhood? Evidence From Indonesia

This research aims to investigate the impact of technology, parental involvement, and educator support on the digital literacy enhancement of young children in Indonesia. A mixed-methods approach was employed, combining surveys, interviews, and document reviews. The research confirmed several key findings: effective use of technology significantly influences digital literacy in early childhood; parental involvement and educator support play pivotal roles in shaping a supportive digital learning environment. Furthermore, the study revealed that access to technology moderates the relationships between these factors and digital literacy enhancement, emphasizing the need for equitable technology access. This study contributes to our understanding of how technology, family involvement, and educator guidance collectively shape young children’s digital literacy. It underscores the importance of fostering effective digital learning environments and ensuring inclusive access to technology. The findings have practical implications for educators, parents, and policymakers in their efforts to promote responsible and safe technology use in early childhood education.
Intelligent Complementary Multi-Modal Fusion for Anomaly Surveillance and Security System

Recently, security monitoring facilities have mainly adopted artificial intelligence (AI) technology to provide both increased security and improved performance. However, there are technical challenges in the pursuit of elevating system performance, automation, and security efficiency. In this paper, we proposed intelligent anomaly detection and classification based on deep learning (DL) using multi-modal fusion. To verify the method, we combined two DL-based schemes, such as (i) the 3D Convolutional AutoEncoder (3D-AE) for anomaly detection and (ii) the SlowFast neural network for anomaly classification. The 3D-AE can detect occurrence points of abnormal events and generate regions of interest (ROI) by the points. The SlowFast model can classify abnormal events using the ROI. These multi-modal approaches can complement weaknesses and leverage strengths in the existing security system. To enhance anomaly learning effectiveness, we also attempted to create a new dataset using the virtual environment in Grand Theft Auto 5 (GTA5). The dataset consists of 400 abnormal-state data and 78 normal-state data with clip sizes in the 8-20 s range. Virtual data collection can also supplement the original dataset, as replicating abnormal states in the real world is challenging. Consequently, the proposed method can achieve a classification accuracy of 85%, which is higher compared to the 77.5% accuracy achieved when only employing the single classification model. Furthermore, we validated the trained model with the GTA dataset by using a real-world assault class dataset, consisting of 1300 instances that we reproduced. As a result, 1100 data as the assault were classified and achieved 83.5% accuracy. This also shows that the proposed method can provide high performance in real-world environments.
An Investigating of Using Dictogloss as an Essential Technique for Improving Student's Listening Comprehension

The current study is an experimental one of using dictogloss as an essential technique with fifth preparatory EFL student's at Al-Saydaa Nafysaa secondary school for girls. The study is done in two classes as a sample: class one is called the experimental class which used dictogloss as an effective technique for improving listening comprehension , whereas the control class is taught by a dictation traditional method. To achieve its intended aims ,there is a test students used, then the data is collected and analyzed using SPSS. Moreover, collected data reveals that experimental class, students were taught using dictogloss which have better comprehension to information heard, and students express positive attitudes towards using dictogloss technique for improving their listening understanding.
An interactive robotic arm with learning function

We have developed a robotic arm for intelligent and automatic gripping of objects. Its working principle is to manually input instructions, and then the system processes the instructions to remove invalid information, and then divides the effective information into two parts: the action and the target object. The action instruction The target command is transmitted to the motion system, and the target command is transmitted to the vision system. The system first pre-processes the collected images, and then performs feature extraction to find the target object. When the target object is confirmed, the vision system uses the principle of binocular ranging to measure out The coordinate parameters of the camera and the target object, the system processes the parameters, obtains the motion parameters of the steering gear and the motor, and then transmits them to the steering gear and the motor. After the steering gear and the motor start, the mechanical arm moves. When the movement stops, the visual The system measures the distance again, and the system judges the measured parameters. If the parameters meet the gripping conditions, the grippers on the robotic arm perform the gripping operation. If the parameters do not meet the gripping conditions, repeat the distance measurement, movement, and measurement. distance until the coordinate parameters of the object and the manipulator meet the gripping requirements.
Joint Client Scheduling and Wireless Resource Allocation for Heterogeneous Federated Edge Learning With Non-IID Data

Federated learning (FL) embraces the concepts of targeted data gathering and training, and it can reduce many of the systemic privacy costs and hazards associated with traditional machine learning frameworks. However, with the low latency requirements of the sixth generation (6 G) wireless communication networks and the Internet of Things (IoT) networks, the convergence delay of FL dramatically influences the overall system performance. In order to solve this urgent and challenging problem, in this paper, a joint client scheduling and wireless resource allocation algorithm is proposed, named SCSBA, which considers system heterogeneity, client heterogeneity, and the fairness of client participation to reduce the latency resulting from the heterogeneous communication conditions and computation capabilities among clients with the non identically independently distributed (Non-IID) data distributions. Specifically, the Stackelberg leader-follower game is first formulated in which the server decides the price of the single quota of participating in the FL process every communication round and the clients decide whether to participate in FL. Then the equilibrium solution of the game is derived and proved. In addition, a bandwidth allocation algorithm based on the covariance matrix adaptation evolutionary strategy (CMA-ES) is designed to minimize the time delay of each communication round. The simulation results verify the effectiveness of the proposed strategy for reducing the time latency of FL processes with heterogeneous clients, i.e., FedAvg and FedOpt.
LORAP: Local Deep Neural Network for Solar Radiation Prediction

Solar radiation directly affects human health and the surrounding environment. Therefore, scientists are paying much attention to this aspect to control the level of radiation. This paper introduces a new model to predict solar radiation using the collected dataset. Our approach focuses on predicting solar radiation frequency with a deep-learning network model. Instead of ideas directly indicating the outcome with one regression model (deep learning or machine learning), we take inspiration from the saying “divide and conquer” to propose a layered learning model. We implement classification models before building local regression models for classes. Our proposal obtains the expected results with $$99\%$$ accuracy for the classification and an MAE of 17.8556 for the regression model. In this paper, we also compare our approach with existing models. Two highlights are: (1) our model is better than several approaches, and (2) it forecasts the ability of solar radiation in the next fifteen minutes based on the current information/data.
Revamping Python for an AI World

Mojo has the same syntax as Python, but runs up to 35,000 times faster.
Development of a Play and Program Mobile Application Based on the Gamification Methodology

Learning the basics of programming is very important in our growing digital age. In the modern world, the education system faces the problem of how to interest students in programming, how to teach them to understand and solve problems. The best way to attract people to any activity, including programming, is to instill interest in the field being studied. Currently, much attention is paid to the introduction of mobile gaming technologies in the educational process. The introduction of education-gaming mobile applications into the educational process increases the motivation of trainees and makes the learning process flexible and accessible. The relevance of the work lies in the development of an education-gaming mobile application for interactive learning of the basics of programming. In an effort to identify the most effective tools for developing mobile devices, the authors analyze existing programming training services that use gamification technologies, on the basis of which they select Unity’s cross-platform development environment and MongoDB databases. The authors developed and tested the educational-gaming mobile application Play and Program, designed to study programming during the game, allowing players to acquire theoretical knowledge, as well as practical skills, track the progress of learning at each stage and maintain interest in learning through a motivational system. This educational-gaming mobile application can be recommended both for self-study of the basics of programming, and for introduction into the educational process of secondary schools and other educational institutions.
AAS: Automatic Virtual Data Augmentation for Deep Image Steganalysis

In recent years, steganalysis based on deep learning has evolved rapidly. However, training deep learning models is data-consuming. The models are prone to overfitting when data is limited. Data augmentation is an effective method to mitigate overfitting. Existing data augmentation methods in steganalysis can be categorized into cover enrichment and virtual augmentation. They are used in different stages. Cover enrichment refers to introducing additional cover-stego pairs in some ways, which is performed prior to training. In contrast, virtual augmentation augments data during training. Existing virtual augmentation methods are designed heuristically and rely on expert knowledge. In this paper, we propose the first automatic virtual data augmentation method for steganalysis. Specifically, we design an augmentation network that augments cover and stego images by intelligently adding noises. The augmentation network is trained adversarially with the steganalyzer to generate diverse data. Meanwhile, a “class-invariant” module prevents the augmentation network from changing the original data distribution too much. A “stabilizer” loss function is designed that keeps the adversarial training stable by constraining the number of noises. The experimental results show that the proposed method outperforms existing virtual augmentation methods. Moreover, combining the proposed method and cover enrichment can further boost performance.
Exploration of Digital Transformation Promotes the Transformation of Teaching Methods

The school has been committed to the construction of a digital learning environment for many years and has developed “five major systems and one platform”, covering the whole process of student growth and curriculum teaching. Through curriculum integration, teaching practice, platform construction, data integration, learning analysis, effective diagnosis, real-time intervention, and other methods, the school focuses on the research and practice of “student digital portrait” based on students’ core literacy and proposes a “three-step, four-style, five-character” digital learning model. It has been widely used.
Practice and Exploration of Digital Transformation of Integrated Education Between Schools and Urban Areas in Hanyang District

Focusing on the general goal of “building an integrated platform of districts and schools, and creating an intelligent education ecology”, Hanyang District has built a large platform of intelligent education of urban schools based on the Wuhan Education Cloud.
Selected Practices Briefs on School Management and Services

Aerospace Longcheng Primary School affiliated to Central China Normal University takes education informatization as the breakthrough point. The school starts with teaching management, teacher management, and logistics management to explore and optimize the development path of school smart management and gradually forms the characteristics of school modernization construction and management. The school relies on the Wuhan Education Cloud Platform to realize the smart management of teachers’ preparation for teaching.
Smart Education Toward the Futures of School

Smart education in the intelligent era is the advanced stage and target form of the development of education informatization. It is a common imagination and expectation for the futures of education, aiming to build smart learning environments, transform the traditional teaching methods, and generate an education system in align with the smart era.
Construction of Future School Based on Smart Space

The school currently has 44 classes, nearly 1600 students, 109 teaching staff, and more than 30% of them have a master’s degree. The objective of the school is “letting every child grow up healthily and happily on Qiangwei campus”, with the “Easy Learning, Efficient Teaching, and Efficient Enriching” as the operating strategy.
The Use of Artificial Intelligence in Digital Marketing Competitive Strategies and Tactics

A variety of applications on handsets, including automatic speech identification, utilize “machine learning,” including internet search engines, spam-filtering mail servers, portals that offer tailored advice, a payment gateway that looks for suspicious items, and online platforms that exert pressure. The structure of the computing world provided for the collection of both contents and the commands needed to alter such material. These early systems were primarily built to conduct mathematical tasks. This reached a stage in which the machine began to interpret the information using a linear equation of an actual framework. The machine had only been obeying commands and had no capacity for learning. Its following phase has been to come up with a series of guidelines that might enable the algorithm to draw its unique conclusions using huge quantities of information and apply such conclusions to categorize and anticipate future information. The discipline of intelligent machines, which is jointly referred to as machine learning, is born as a result of “artificial intelligence.”