Internet Technology Research Articles

Research on Dance Education in Universities Based on Online and Offline Blended Teaching

With the rapid development of educational informatization, the transformation of teaching methods has become an inevitable requirement for the long-term development of education, which also puts forward higher requirements for the work of art education in universities. With the development of network technology, online and offline hybrid teaching modes came into being. This mode uses the Internet, cloud computing and other technologies to provide students with a diversified and adaptable expansion teaching mode. As a branch of art teaching in universities, college dance should focus on innovative teaching methods, construct a blended online and offline teaching model, broaden educational channels, and enrich educational resources. Based on this, the article analyzes and studies the blended teaching of online and offline dance education in universities, explores the advantages and challenges of blended dance teaching, analyzes the necessity of blended teaching in online and offline dance teaching in universities and proposes specific educational strategies for reference.

Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies

In the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching.

Research on movie recommendation algorithms based on machine learning

In recent years, the number of movies released worldwide has grown exponentially. Due to the large number of movies, it is difficult for users to find movies that match their preferences. Therefore, with the development of Internet technology, it has become an important research direction how to filter out the movies that users are interested in from the massive movie data. This paper mainly focuses on the film recommendation algorithms based on machine learning, including the traditional collaborative filtering algorithm, rating-based sorting recommendation algorithm and content-based recommendation algorithm. By conducting a detailed analysis of the principles, advantages and disadvantages, as well as application scenarios of these recommendation algorithms, this paper aims to identify methods that best fit current movie recommendation systems. The objective is to improve the real-time and personalized recommendation, while providing users with better film recommendation services.

Characteristics, Causes and Countermeasures for Victims of Financial Fraud

With the rapid development of Internet technology and the increasing popularity of smart phone applications, the problem of financial fraud has become increasingly prominent, and people of different ages are at risk, which has caused serious impact on society. Victims of financial fraud are usually affected by herd mentality, loss aversion, confirmation bias, etc., while scammers use false publicity to induce investors to make their assets suffer losses. After in-depth analysis of the causes of fraud, this study believes that improving public financial literacy and strengthening financial education become the key to prevent fraud in the whole society. Specifically, college students should deepen their understanding of financial knowledge, and middle-aged and elderly people should improve their awareness of network security and basic knowledge of financial management. Through in-depth research and comprehensive policies, it is expected to build a safe and honest financial environment, ensure the property safety of people of all ages, and promote stable and healthy economic development.

On the Promotion of E-commerce on International Trade- Analysis Based on the Background of Digitalization

With the continuous development of Internet technology, the global economy is getting closer and closer, and e-commerce emerges. As a new way of economic and trade, e-commerce provides an unlimited market platform for international economic and trade, bringing unprecedented opportunities for international trade participants. However, the development of e-commerce is also accompanied by various problems. This article takes the impact of e-commerce on international economic and trade as the starting point, analyzes the positive impact of e-commerce in international trade, explores the challenges brought by it, and give play to the role of Silk Road e-commerce in the background of China's "Belt and Road". Promote the healthy development of international economic and trade.

A lossless probabilistic image encryption algorithm based on QSD decomposition and matrix transformations

With the development of Internet technology, images have become widely used in people's daily life. However, some images carry significant importance and sensitivity, making the security and protection of digital information more crucial. Standard encryption schemes such as RSA, DES, and AES, which have proven effective for textual data, face limitations when applied to images due to their unique features, such as large data capacity and strong pixel correlation. In recent years, numerous effective image encryption schemes utilizing diverse techniques have been proposed. However, the use of integer decomposition in encryption techniques is still underexplored. This paper introduces a novel, lossless, and probabilistic image encryption scheme designed to enhance the security of sensitive images during transmission and storage. This scheme is based on a new integer decomposition technique and utilizes a key with a very large key space. In the proposed approach, an image is encrypted into a pair of images using quasi-square decomposition and new matrix transformations. Security is further reinforced through the use of orthogonal matrices, which enhance diffusion while ensuring data integrity and preservation. Experimental results demonstrate that the proposed scheme is highly secure against most known attacks and cryptanalysis methods. Additionally, a comparison with various existing image encryption techniques shows the effectiveness of the proposed approach. This research provides a significant contribution by introducing new techniques that leverage integer decomposition, which are highly effective against CPA (Chosen Plaintext Attack) and KPA (Known-Plaintext Attack). Moreover, these techniques enhance the entropy value of encrypted images and improve the values of NPCR and UACI, which help prevent differential attacks. The proposed scheme has the potential to advance applications in secure image transmission, storage, and privacy protection.

Building a web-based Comprehensive School Portal System Using the CodeIgniter Framework

With the various advantages and conveniences that the internet can provide, various fields of human activity have begun to use internet technology to help facilitate the activities they undertake. Likewise with the education sector in managing its administration. The web is one of the internet technologies that is widely used to facilitate monitoring of school activities because it can be accessed easily anywhere and at any time, from school facilities to school activities. Through the use of a web data system, problems involving redundancy of student information, insynchronization between existing information and slow production of information can be overcome. By designing an information system or web-based portal, you can increase performance effectiveness.

Augmented Reality for Science Learning in the 21st Century: Systematic Literature Review

The Era of Revolution 4.0 is marked by the internet and digital technology development. Era of Revolution 4.0 is believed to affect changes in science learning which can train thinking skills of the 21st century. 21st Century Learning emphasizes learning for students on internal skills using Media, Information, and Technology; Thinking, and innovation skills. Augmented Reality is a technological innovation that is currently developing and is being used in science learning by several researchers to train 21st-century skills. The research aims to examine Augmented Reality for Science Learning in abd 21: Systematic Literature Review. The review was conducted based on state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. The results of this research explain the focus of 21st Century learning; Augmented Reality has several advantages, Utilization of Augmented Reality for 21st Century science learning. The results of this research explain that the focus of 21st Century learning is: Competency for Critical Thinking, Innovation and Creativity, Communication, and Cooperation; Augmented Reality has several advantages and disadvantages: advantages; Is more involved, Easy to use, Can be used in general in various sources, Describe simple objects, Production that doesn't consume too many lots of expenses, Simple to use; disadvantages: sensitive to changes in perspective, There are few makers, Requires a lot of memory to Utilization of Augmented Reality: Competency for Critical Thinking, Ability to think Abstractly, Ability to Think Creatively.

Impacts of blended learning with BOPPPS model on Chinese medical undergraduate students: a comprehensive systematic review and meta-analysis of 44 studies

BackgroundWith the development of Internet information technology, especially the impact of the sudden outbreak of the novel coronavirus disease 2019 epidemic, along with the call for “classes suspended but learning continues,” a large number of medical educators have learned and experienced online teaching. They have understood the shortcomings of traditional teaching methods. Not only the blended BOPPPS teaching mode combines the advantages of the BOPPPS teaching mode but also the online teaching platform breaks through the limitation of time and space. However, a general consensus on the effectiveness of the blended BOPPPS teaching strategy in China is lacking, and few studies use quantitative synthesis to evaluate the effectiveness of this teaching strategy. Hence, this study aimed to assess the overall effectiveness of online and offline blended BOPPPS teaching strategies in higher medical education in China compared with the lecture-based learning (LBL) teaching model.MethodsStudies that blended learning with the BOPPPS model in China from January 2000 to October 2023 were searched in the Chinese and English-language online databases. We analyzed the objective and subjective scores of students and performed subgroup analysis for specialties and online teaching platforms. The data were analyzed using the Stata version 14.0 software. The quality assessment was performed using the Jadad scoring scale.ResultsForty-four studies were included in this meta-analysis. Compared with the LBL mode, the blended the BOPPPS teaching mode was more effective in terms of the overall capacity [standardized mean difference (SMD) = 1.193, 95% confidence interval (CI): 0.813–1.572], mastery of medical theory knowledge (SMD = 1.090, 95% CI: 0.730–1.450), and practical skills (SMD = 1.246, 95% CI: 0.799–1.693). The analyzed questionnaire surveys indicated the positive effects of the blended BOPPPS teaching mode on classroom satisfaction, autonomous learning ability, learning interest, teamwork ability, interpersonal skills, ability to analyze and solve problems, group interaction, learning engagement, and learning strategies.ConclusionsThe study underscored that the blended BOPPPS teaching mode could effectively improve the comprehensive quality of students. The subjective scores also indicated that students generally preferred this novel teaching mode.

A call to action: Designing a more transparent online world for children and young people

This paper reports on a qualitative research study that explored the practical and emotional experiences of young people aged 13–17 using algorithmically-mediated online platforms. It demonstrates an RI-based methodology for responsible two-way dialogue with the public, through listening to young people's needs and responding to their concerns. Participants discussed in detail how online algorithms work, enabling the young people to reflect, question, and develop their own critiques on issues related to the use of internet technologies. The paper closes with action areas from the young people for a fairer, usefully transparent and more responsible online environment. These actions include a desire to be informed about what data (both personal and situational) is collected and how, and who uses it and why, and policy recommendations for meaningful algorithmic transparency and accountability. Finally, participants claimed that whilst transparency is an important first principle, they also need more control over how platforms use the information they collect from users, including more regulation to ensure transparency is both meaningful and sustained.

Research on the distribution mode of Hema supermarket under the new retail background

With the continuous development of big data, internet technology, and the ever-evolving consumer demands, the concept of "new retail" has emerged, imposing more rigorous standards on various aspects of retail businesses. As a critical component of "new retail," the delivery model is the main focus of this paper, with a specific analysis of the delivery model of Hema Supermarket. It examines the current state of the delivery model and proposes optimization solutions for Hema Supermarket's product delivery model. The goal is to enhance the efficiency of the delivery model, thus improving the economic benefits of Hema Supermarket and facilitating its growth.

Experience of Taiwan-Russian healthcare cooperation model building using Internet technologies

Experience of Taiwan-Russian healthcare cooperation model building using Internet technologies

Strategy for Improving the Vitality of College Youth League Branch from the Perspective of Self-media

With the vigorous development of Internet information technology, various forms of self-media have emerged as one of the main channels for information acquisition and dissemination. While this provides opportunities for the work of college Communist Youth League branches, it also brings significant challenges. They need to adjust their work mode in accordance with the characteristics of self-media in order to continuously enhance their vitality. However, at present, there are still many problems in the work of college Communist Youth League branches, which significantly deviate from the requirements of the self-media era. This results in a low vitality of the branches and an inability to fully play their role. Therefore, further optimization and improvement are necessary. Based on this, this article briefly introduces the concept and characteristics of self-media, analyzes in detail the opportunities and challenges brought by self-media to college Communist Youth League branches, explores the prerequisites for enhancing the vitality of college Communist Youth League branches with the help of self-media, and finally proposes several suggestions for enhancing the vitality of college Communist Youth League branches from the perspective of self-media. It is hoped that these suggestions can promote the improvement of organizational and operational capabilities of college Communist Youth League branches and provide a reference for fulfilling their political mission and educational value.

APCSMA: Adaptive Personalized Client-Selection and Model-Aggregation Algorithm for Federated Learning in Edge Computing Scenarios.

With the rapid advancement of the Internet and big data technologies, traditional centralized machine learning methods are challenged when dealing with large-scale datasets. Federated Learning (FL), as an emerging distributed machine learning paradigm, enables multiple clients to collaboratively train a global model while preserving privacy. Edge computing, also recognized as a critical technology for handling massive datasets, has garnered significant attention. However, the heterogeneity of clients in edge computing environments can severely impact the performance of the resultant models. This study introduces an Adaptive Personalized Client-Selection and Model-Aggregation Algorithm, APCSMA, aimed at optimizing FL performance in edge computing settings. The algorithm evaluates clients' contributions by calculating the real-time performance of local models and the cosine similarity between local and global models, and it designs a ContriFunc function to quantify each client's contribution. The server then selects clients and assigns weights during model aggregation based on these contributions. Moreover, the algorithm accommodates personalized needs in local model updates, rather than simply overwriting with the global model. Extensive experiments were conducted on the FashionMNIST and Cifar-10 datasets, simulating three data distributions with parameters dir = 0.1, 0.3, and 0.5. The accuracy improvements achieved were 3.9%, 1.9%, and 1.1% for the FashionMNIST dataset, and 31.9%, 8.4%, and 5.4% for the Cifar-10 dataset, respectively.

Designing a Robust Quantum Signature Protocol Based on Quantum Key Distribution for E-Voting Applications

The rapid advancement of internet technology has raised attention to the importance of electronic voting in maintaining democracy and fairness in elections. E-voting refers to the use of electronic technology to facilitate the casting and counting of votes in elections. The need for designated verification arises from concerns about voter privacy, auditability, and the prevention of manipulation. Traditional e-voting systems use cryptographic techniques for security but lack verifiable proof of integrity. Integrating e-voting with a quantum designated verifier could address these challenges by leveraging the principles of quantum mechanics to enhance security and trustworthiness. In light of this, we propose a quantum e-voting scheme that uses a designated verifier signature. To ensure the confidentiality and authenticity of the voting process, the scheme uses quantum features like the no-cloning theorem and quantum key distribution. The proposed scheme has security properties like source hiding, non-transferability, and message anonymity. The proposed scheme is resistant to many quantum attacks, such as eavesdropping and impersonation. Due to designated verification, the scheme minimizes the risk of tempering. This paper provides a detailed description of the proposed scheme and analyzes its security properties. Therefore, the proposed scheme is efficient, practical, and secure.

Internet-Based Telerehabilitation Guidance Brings Additional Benefits to Patients after Percutaneous Coronary Intervention: A Retrospective Cohort Study

Purpose: This study aimed to analyze the value of remote rehabilitation guidance based on Internet technology in patients undergoing percutaneous coronary intervention. Methods: We retrospectively analyzed the clinical data of 253 patients who underwent percutaneous coronary intervention in our hospital from June 2022 to March 2023. According to different nursing methods, they were divided into a study group (remote rehabilitation guidance based on Internet technology) and a control group (routine follow-up management) with 124 and 129 cases, respectively. The compliance left ventricular diastolic dysfunction (LVDD), left ventricular end-systolic diameter (LVDS), left ventricular ejection fraction (LVEF), Seattle Angina Questionnaire (SAQ), self-rating depression scale (SDS), self-rating anxiety scale (SAS), self-care ability, quality of life (SF-36), and satisfaction of the two groups were compared. Results: The compliance of the study group was 94.35%, which was higher than that of the control group (77.52%; n = 100, χ2 = 14.683, p < 0.001). We found no difference in cardiac function between the groups before the intervention (z = –0.783, p > 0.05). After the intervention, the LVDD and LVDS of the study group were lower than those of the control group, but LVEF was higher than that of the control group (z = –9.645, p < 0.001). Before the intervention, there was no difference in the SAQ scores between the groups (z = –0.180, –1.260, and –0.543, p > 0.05). After the intervention, the scores of angina pectoris attack, disease stability, and disease cognition in the study group were higher than those in the control group (z = –13.679, –10.644, and –11.448, p < 0.001). There was no difference in SDS and SAS scores between the groups before the intervention (z = –0.008 and –0.717, p > 0.05). After the intervention, the scores of the research group were lower than those of the control group (z = –13.709 and –8.041, p < 0.001). Before the intervention, we found no difference in self-care ability between the groups (z = –0.675, p > 0.05). After the intervention, the scores of health knowledge, self-responsibility, self-concept, and self-care skills in the study group were higher than those in the control group (z = –11.644, –9.387, –12.612, and –12.012, p < 0.001). There was no difference in the SF-36 scores between the groups before the intervention (z = –0.682, –0.189, –1.124, and –0.018, p > 0.05). After the intervention, the scores of the study group were higher than those of the control group (z = –12.323, –12.163, –12.066, and –12.054, p < 0.001). The satisfaction rate of the research group was 91.94%, which was significantly higher than that of the control group (70.54%; n = 91, χ2 = 18.822, p < 0.001). Conclusions: The effect of remote rehabilitation guidance based on Internet technology is evident. It can effectively improve patients' cardiac function, enhance self-care ability, and result in high compliance with nursing care. Moreover, it can effectively reduce negative emotions and improve their quality of life. Given that satisfaction is high, it is a procedure worth promoting.

Diffusion of fashion trend information: a study on fashion image mining from various sources

The advancement in the internet and mobile technologies has substantially altered information diffusion in modern society, creating a diverse environment for generating and sharing various forms of information. Specifically, the emergence of new information sources, such as influencers and online communities, has significantly influenced the formation of consumer opinion. We highlight the changes that have occurred in the diffusion of fashion trend information. To do this, we conducted data mining, which involved three main steps: data preprocessing, specifically converting image data (including images from the 2022 F/W season runway collection, fashion influencer outfits, and best items from online fashion retailers) into textual data; data mining analysis (quantitative analysis); and data post-processing. As a result, we found that even items with low or no appearance on the runway held significance in the best item data or fashion influencer outfits. Specifically, the best items on online fashion retailers, reflecting popular fashion trends, had greater similarity to fashion influencer outfits. However, similarities in silhouette attributes were found among runway collections, fashion influencer outfits, and best items data. This study holds great significance because it focuses on fashion items genuinely consumed by the mainstream consumers rather than only focusing on the four major runway collections. Furthermore, these findings offer valuable insights for merchandising and trend forecasting, emphasizing the importance of selectively utilizing fashion trend information in the planning of fashion products.

IDAD: An improved tensor train based distributed DDoS attack detection framework and its application in complex networks

With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency. To cope with it, three key strategies are implemented in this paper: (i) Using tensors to model large-scale and heterogeneous data in complex networks; (ii) Proposing a denoising algorithm based on the improved and distributed tensor train (IDTT) decomposition, which optimizes the tensor train(TT) decomposition in terms of parallel computation and low-rank estimation; (iii) Combining (i), (ii) and Light Gradient Boosting Machine (LightGBM) classification model, an efficient DDoS attack detection framework is proposed. Datasets CIC-DDoS2019 and NSL-KDD are used to evaluate the framework, and results demonstrate that accuracy can reach 99.19% while having the characteristics of low storage consumption and well speedup ratio.

An improved performance of reversible data hiding in encrypted images using decision tree algorithm

The advancements in increase of internet technology, transfer of data through insecure channel has become more popular. Now a day's most of the customers were turned to digital world and the channel is the main resource of communication. There is no guarantee that stored data will not be accessed by any hacker or cloud service provider. Privacy and Security is the main aspect of the rapid development in the digital storage. These conditions demand to make sure that the data is encrypted, stored and transmitted. Reversible Data Hiding in Encrypted images using machine learning is an emerging methodology used in the domain to secure the information for end-to-end secure data transmission. In this proposed method, all the data's can be Encrypted using Digital Keys, and these data can be embedded in cover images using decision tree algorithm. In the receiver side, the data can be retrieved back if receiver have right keys which is used while encryption. This system uses pseudo random number generation for generating multiple keys to encrypt and decrypt the secret data. This method is designed for encryption the Images and Text data. To increase further security and embedding capacity a machine learning algorithm is also implemented. The proposed Decision Tree method is tested on the standard image processing images and certain parameters are calculated for performance metrics and compare the results with existing schemes. The Algorithm is implemented in MATLAB and used the Statistics and Machine Learning library in MATLAB.

Encoder-Decoder Transformers for Textual Summaries on Social Media Content

Social media has a leading role to our lives due to radical upgrade of internet and smart technology. It is the primary way of informing, advertising, exchanging opinions and expressing feelings. Posts and comments under each post shape public opinion on different but important issues making social media’s role in public life crucial. It has been observed that people's opinions expressed through social networks are more direct and representative than those expressed in face-to-face communication. Data shared on social media is a cornerstone of research because patterns of social behavior can be extracted that can be used for government, social, and business decisions. When an event breaks out, social networks are flooded with posts and comments, which are almost impossible for someone to read all of them. A system that would generate summarization of social media contents is necessary. Recent years have shown that abstract summarization combined with transfer learning and transformers has achieved excellent results in the field of text summarization, producing more human-like summaries. In this paper, a presentation of text summarization methods is first presented, as well as a review of text summarization systems. Finally, a system based on the pre-trained T5 model is described to generate summaries from user comments on social media.