Continuous Development Of Network Technology Research Articles

Design of an online education student learning status evaluation model based on dual-improved neural networks

With the continuous development of network technology, online education has become an important form of education. However, in the online education model, it is difficult for educators to effectively evaluate students' learning status, and using a learning status evaluation model can effectively solve this problem. The main goal of this model is to comprehensively evaluate students' learning behavior, progress, and outcomes, in order to understand their learning status, provide effective teaching feedback to teachers, help students improve learning methods, and improve learning efficiency. The current automatic evaluation model for student learning status has certain limitations in terms of applicability and accuracy. A student learning state evaluation model based on Multi task Cascaded Convolutional Networks (MTCNN) is proposed to address the effectiveness of online education student learning state evaluation. Use the facial image acquisition function to extract students' facial features, process each feature through label classification, and then analyze the students' attention and learning emotions. Finally, analyze the effectiveness of the research method application. The results showed that the train_loss value of the learning state evaluation model proposed in the study can be reduced to about 0.1; the train_acc value can reach more than 95 %, and the overall volatility is small; the overall evaluation accuracy of facial expressions can reach 74.71 %, which is significantly better than cpc, VGG19 and other evaluation methods; compared with the comprehensive evaluation results and multi-modal analysis methods, only two evaluations at the critical value are different. The experimental results show that the online education students’ learning status evaluation model designed by the research has a high accuracy rate and has a certain application potential in the field of online education.

Research on the Current Situation of Cybercrime and Criminal Law Regulation Issues

With the continuous development of network technology makes the Internet gradually into thousands of households, the network has increasingly become an indispensable part of people's work and life. In recent years, in the judicial practice of our country, there are a lot of cases about network crime, which makes people have to pay attention to the potential Internet security problems while enjoying the convenience and speed brought by the Internet. Followed by Internet crime cases to get people to re-examine network problems, now the network crime of technology advancement, the degree of complexity and concealment and difficult to obtain evidence is completely different to traditional crime, it also shows our country at present in the network crime in the criminal law on the regulation of some deficiencies, it is necessary to strengthen the regulation of network crime of criminal law, combining with the concrete problems in judicial practice make effective regulation measures, such as constructing cyber crime legislation system, perfecting the relevant charges system, improve the network technology and strengthen to the improvement of the network security system, improve the content of the social public network moral quality, So that citizens can actively participate in crime prevention and control, so that the criminal law regulation measures to play a real and effective role in curbing cybercrime.

An exploration and practice of an optimized online annotation solution based on PDF slicing and SVG

With the continuous development of network technology, online annotation has become an essential tool in fields such as education, training, and document collaboration. However, traditional annotation techniques are plagued by several limitations, including low security, non-erasable annotations after uploading, and difficulties in communication across different systems. To address these issues, this paper proposes an SVG-based online annotation technique that is built upon PDF file slicing. This approach aims to enhance the deficiencies of traditional annotation techniques and improve user interaction. The research begins by introducing the background and significance of online annotation technology, elucidating the application prospects of PDF slicing technology and SVG in annotation. The paper then provides a detailed description of the annotation algorithm and the implementation process. Additionally, it discusses the experimental setup, results, and analysis. The results indicate that the SVG-based online annotation technology can effectively enhance annotation efficiency and real-time interaction while enabling erasure of historical data and facilitating cross-platform communication. Compared to traditional annotation techniques, the SVG-based approach exhibits higher practicality and flexibility, making it suitable for various types of document annotations and collaborative scenarios.

Definition of the Presumption Rule of "Knowledge" in the Crimes of Assisting in Information Network-Related Criminal Activities

The continuous development of network technology has profoundly transformed the daily production and lives of human beings. However, it has also given rise to numerous new forms of cybercrime, leading to the proliferation of certain cybercrimes. Assisting information network criminal activities constitute a crucial aspect of cybercrime. In 2015, the promulgated Criminal Law Amendment (IX) listed it as an independent offense. However, due to the problem of unclear judicial interpretation, the crime has been in a state of cold storage for a long time until 2019, when the Supreme Peoples Court and the Supreme Peoples Procuratorate promulgated the Interpretation of a Number of Issues Concerning the Application of Law to the Handling of Criminal Cases of Illegal Utilization of Information Networks and Helping Criminal Activities on Information Networks and Other Criminal Cases the crime has gradually begun to be applied in large quantities. In recent years, the crimes of assisting in information network-related criminal activities has been on the rise, but there have been numerous objections and lack of consensus regarding the determination of subjective knowledge in judicial practice and the theoretical community. Therefore, it is crucial to clarify the presumption rule of knowledge to provide guidance for judicial practice.

A Secure Information Transmission Protocol for Healthcare Cyber Based on Quantum Image Expansion and Grover Search Algorithm

In the healthcare cyber, the transmission of information between the head office and each sub-branch has always been a very important process. However, with the continuous development of network technology in recent years, its security has also been subjected to greater and greater tests. Based on quantum image expansion technology and Grover search algorithm, this paper proposes a more secure and large-capacity secure information transmission protocol for healthcare cyber. The new protocol embeds the secret information through three layers and encrypts it at the same time. Finally, the secret information is transmitted through quantum channel in the form of quantum state, which has very high security. At the same time, the new protocol is essentially a novel quantum image steganography protocol. The imperceptibility of the steganography protocol is essentially a higher level of security, which can effectively avoid various attacks and further guarantee the security of the information transmission process.

Digital Marketing of Museum: How UCCA Achieves Audience Strategy on Red and Tiktok in China

Over the last decade, the continuous development of network technology contributes to prosperous digital marketing. At the same time, the advancement of network technology has also revolutionized the lifestyle of the public.The relationship between business and audience has transformed into a more interactive nature through social media in the era of digital marketing. Therefore, this report aims, by using one of the most esteemed muse-ums in China UCCA as a model, to conduct research about how the museum industry practices its audience-oriented strategy in Chinese social media platforms, Tik Tok and Red Book. After analyzing UCCAs approach on Tik Tok and Red Book, this report gives out some recommendations based on the findings.

Research on Influencing Factors of Public Opinion Information Forwarding Behavior of Generation Z Group

[Purpose/Significance] With the continuous development of network technology, the ways of information dissemination become more and more diversified, and the threshold of information dissemination becomes lower and lower. This not only facilitates people's life, but also brings information fog, information polarization and other problems, causing great harm to the network environment. Through the research on the forwarding behavior of Z generation group's public opinion information, this study finds out the influencing factors, so as to help relevant departments improve the network environment and put forward new ideas for the management and control of online public opinion information. [Method/Process] In this study, through questionnaire survey and semi-structured interview, a sample survey was conducted on the "group in Hefei, and the grounded theory was analyzed with the help of Nvivo12 software. A comprehensive research model of influencing factors of public opinion information forwarding behavior of Generation Z group was constructed, and the characteristics of Generation Z group were analyzed. [Result/Conclusion] The research shows that information content mediates the attributes of information receivers. The intermediary role of consumers' internal state in the process of e-commerce anchor attribute influencing consumers' online purchase intention; The external environment plays a regulatory role in the transmission chain of public opinion information.

A Study on the Dilemma of People's Legal Awareness Caused by the Communication of Public Crisis Events in the Social Media Era: The Beating Incident in Tangshan as an Example

With the continuous development of network technology, the most intuitive social result brought by the development of media iss the absolute amount of information increase, we have entered a brand new information society. The production and dissemination of information are not only the key to economic development but also exert an important influence on politics and culture. This paper takes the case of the Tangshan Hitting Incident as an example to analyze the definition, principle, characteristics and dilemma of public crisis communication in the era of social media, as well as the dilemma and analysis of people's awareness of the rule of law. To provide suggestions for better communication of public crisis events in the era of social media, and to efficiently solve the dilemma of communication and legal awareness.

Reliability analysis of the generalized balanced hypercube

With the rapid and continuous development of network technology, a multiprocessor system, consisting of multiple processors and communication links, plays an significant role in big data era. The topology of a multiprocessor system is a basic architecture of great influence on the network performance. Multiprocessor systems should have some good performances such as low communication costs, high network reliability, and friendly resource sharing. Therefore, designing a high-performance network architecture is of great significance to multiprocessor systems. In this paper, we propose a more general class of balanced hypercubes with two more general edge-disjoint sets of perfect matchings as cross edges. Then we explore some basic properties of GBHn. Furthermore, we show the connectivity, super connectivity, extra connectivity and (extra) diagnosability of GBHn, respectively.

Contrastive Learning Enhanced Intrusion Detection

With the continuous development of network technology, the diversity of network traffic constantly increased (intra-class diversity). Nevertheless, the boundary between malicious and benign actions became even ambiguous (inter-class similarity), causing lots of false detection and hindering the further optimization of the detection model. Focusing on challenges brought by intra-class diversity and inter-class similarity, we proposed a novel approach to enhance intrusion detection based on contrastive learning, which can make the right decision while disentangling samples from different classes. First, to bridge the gap when applying contrastive learning to intrusion detection data, we proposed a heuristic method to build contrastive tasks based on random masking of network packet sequences, which can reflect semantic relationships among samples. Then contrastive loss can be calculated to measure the inter-class and intra-class distances. Second, contrastive cross-entropy loss was proposed, which was a combination of contrastive loss and classification loss. Together with a dual branch deep structure, we can optimize the detection and sample distance requirements at the same time. Thirdly, experiments were conducted on diverse real-world and benchmark datasets using different model architectures under various parameter settings. Results on real-world dataset showed that our methods could stable experience a 5% increase in accuracy, and an 8% improvement in detection rate on those easily misdetected scenarios. To verify the method’s effectiveness on different traffic representations, we further conducted experiments on NSL-KDD and UNSW-NB15, which achieved a 7% accuracy improvement on NSL-KDD and a 6% accuracy improvement on UNSW-NB15. Extensive comparison with state of art intrusion detection models in recent five years showed that the proposed methods could effectively improve the accuracy of detection models.

Privacy Law Protection Based on the Information Security Assurance Algorithm.

With the continuous development of network technology, the production and lifestyle of human beings are also quietly changing. People find that the network makes life faster and more convenient and also makes our life more “transparent.” The openness, sharing, and convenience of the network make it easier to infringe on the privacy rights of others, bringing an unprecedented impact on traditional privacy protection. This paper studied information security assurance-related algorithms and built an information security assurance model. Through this model, some privacy violation events existing in the network today were analyzed. Then, a corresponding questionnaire was constructed according to some of the problems of these events. The questionnaire is mainly used for research on people of all age groups, mainly to study their understanding of relevant legal knowledge and their views on the law. A total of 188 valid questionnaires were collected in this survey. Only 37.23% people knew a lot about the law. Most of them learned it from the Internet, and they rarely learned about it from relevant books. About 70% people believe that the law plays a big role in privacy protection, and they will choose legal help if they encounter privacy violations. However, this is inconsistent with some data obtained from the information security assurance model. Through analysis, it can be seen that although they want legal help, they lack the relevant knowledge of the law, and they do not know whether the privacy of individuals has been violated. Therefore, it is necessary to strengthen personal knowledge of privacy protection laws and improve the personal information legal protection and fair use system.

A Blockchain-Based Multi-CA Cross-Domain Authentication Scheme in Decentralized Autonomous Network

The continuous development of network technology has driven the emergence of smart devices, and the demand for smart devices interconnection has increased sharply, which requires the identity of devices to be authenticated to carry out secure communication. The traditional certificate-based identity authentication scheme can no longer meet the authentication requirements of massive devices. As an authority that issues and manages certificates, Certificate Authority (CA) creates data islands of intra-domain certificates, increasing the complexity of cross-domain authentication. In order to improve the efficiency of cross-domain authentication, this paper introduces blockchain technology, which can establish trust in an untrusted environment. We propose a multi-CA-based authentication architecture to establish distributed trust and share cross-domain certificate information among multiple domains. On this basis, we design a simplified identity authentication scheme to quickly complete cross-domain identity authentication and reduce authentication overhead. To further improve the efficiency of cross-domain authentication, a cross-domain certificate revocation mechanism is designed. The scheme has passed the formal security analysis, and the simulation results show that the cross-domain authentication scheme is efficient.

A data skew-based unknown traffic classification approach for TLS applications

With the continuous development of network technology, the volume of encrypted traffic from unknown applications rises sharply, posing a significant challenge to conventional traffic classification methods. While these methods achieve a certain level of success in recognizing specific application traffic, they fail to classify unknown traffic, especially encrypted traffic. Existing traffic classification methods are usually constrained by the assumption that classes encountered in testing are also present in training, which is not consistent with the open environment of the real world. In this paper, we propose a novel data skew-based classification method for Transport Layer Security (TLS) application unknown traffic (DSCU) to achieve consistent classification of TLS applications. First, DSCU constructs skew data, and then the one-class classifiers generated based on the skew data limit the input space scope of the known class and reserve space for the unknown class. This enables DSCU to separate known flows (i.e., flows from applications contained in the training set) from unknown flows (i.e., flows without any application information regarding them during training). After separation, the fine-grained classification of known flows can improve the accuracy of known flow classification. Three groups of experiments conducted on a real-world dataset covering 25 applications show that DSCU reliably achieves outstanding performance on TLS flow classification.

Persistence and innovation of traditional broadcast hosts in the digital age

Today, with the continuous development of network technology, digital technology, and media technology, telecommunication networks, the Internet, and radio and television networks are gradually integrated, and the digital media era has officially arrived. In this context, this paper analyzes the inheritance and innovation of broadcasting and hosting, and puts forward feasible strategies for inheritance and innovation for reference after a brief understanding of the development status of broadcasting and hosting.

Connotation and Determinants of Web Collective Intelligence

With continuous development of network technology, users in network community are promoted to interact deeply, and remarkable web collective intelligence emerges in the process. As a relatively new concept, the connotation of web collective intelligence is preliminarily explored in this paper, where the network community is taken as the environment, expert users as the subject, and web comments as the carrier. Meanwhile, taking Wikipedia as an example, by means of questionnaire survey and structural equation model, a more systematic index system is constructed from the perspective of user characteristics to explore determinants of web collective intelligence quality, and potential influence of user attributes on user behavior.

Research on Ideological and Political Teaching Reform of E-commerce Specialty Marketing Course

The ideological and political education of college students is an important part of all teaching tasks at present. For students majoring in e-commerce, it is also necessary to strengthen the learning of ideological and political teaching tasks while studying marketing courses. The ideological and political quality of college students is directly related to their future employment development and the correct formation of their own life values. As the core force of the future development of the motherland, college students have higher and higher requirements for political and ideological understanding. Especially nowadays with the continuous development of network technology, the major of e-commerce has become a popular major. In order to bring a better future to the e-commerce industry, it is particularly urgent to strengthen the content of ideological and political education of college students. This article is based on such an era background, to analyze the relevant strategies of ideological and political teaching reform in marketing courses.

Efficacy of online and face-to-face cognitive behavioral therapy in the treatment of neurological insomnia: a systematic review and meta-analysis.

The treatment of insomnia mainly includes drug therapy and non-drug therapy (cognitive behavioral therapy [CBT]). Traditional face-to-face CBT is affected by factors such as location, time, and treatment cost, making the treatment impossible to implement effectively. With the continuous development of network technology, internet-based CBT (ICBT) has been widely used due to the advantages of time and location. It searched the China National Knowledge Internet (CNKI) database (1979-Apr 2021), China Biomedical Literature Database (1994-Apr 2021), Cochrane Library (2005-Apr 2021), Medline (1948-Apr 2021), and Embase (Jan 1966-Apr 2021). Chinese and English databases were searched using the following terms: internet-based cognitive behavioral therapy, cognitive behavioral therapy, sleep problems, and sleep disorders. Meta-analysis was performed using RevMan 5.3 and Stata SE 12.0 software provided by the Cochrane collaboration. A total of 14 randomized controlled trials were included in this study. Of these, 10 described the correct random allocation method, 6 described the allocation scheme in detail, and 1 article used the blind method. Sleep onset latency after ICBT was much shorter than that of the control group [mean difference (MD): -12.27, 95% confidence interval (CI): -16.43 to -9.90, P<0.01]. Total sleep time after ICBT was much longer than that of the control group (MD: 38.67, 95% CI: 34.70-42.65, P<0.01). Sleep efficacy after ICBT was significantly higher in contrast with the control group (MD: 13.28, 95% CI: 10.49-16.06, P<0.01). Anxiety and depression levels after the ICBT were significantly lower than those in the control group (P<0.01). Meta-analysis was adopted to confirm that ICBT can greatly improve the sleep parameters of patients with insomnia, and it was found to have a relieving effect on patients with anxiety and depression.

Representativeness-Based Instance Selection for Intrusion Detection

With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situations, some selected instances are redundant and some important instances are removed, increasing storage space and reducing efficiency. Therefore, a new representativeness of instance is proposed and considers not only the influence of all instances of the same class on the selected instance but also the influence of instances of different classes on the selected instance. Moreover, it considers the influence of instances of different classes as an advantageous factor. Based on this representativeness, two instance selection algorithms are proposed to handle balanced and imbalanced data problems for intrusion detection. One is a representative-based instance selection for balanced data, which is named RBIS and selects the same proportion of instances from each class. The other is a representative-based instance selection for imbalanced data, which is named RBIS-IM and selects important majority instances according to the number of instances of the minority class. Compared with other algorithms on the benchmark data sets of intrusion detection, experimental results verify the effectiveness of the proposed RBIS and RBIS-IM algorithms and demonstrate that the proposed algorithms can achieve a better balance between accuracy and reduction rate or between balanced accuracy and reduction rate.

Research on Computer Assisted English Teaching in Foreign Language Teaching in Colleges

The continuous development of network technology in the information age has provided hardware guarantees for networked teaching, and the deepening of computer-assisted teaching research has promoted the development of networked computer-assisted teaching. With the popularization and application of informatization in colleges and universities, the use of modern information technology to change the traditional teaching mode has become the focus of attention. The C++ network-assisted teaching system uses computers and networks as the main means to develop network-based teaching and learning auxiliary teaching methods. This system is a mixed mode of network teaching and traditional teaching, which can enhance the communication between teachers and students, and can also play the role of the student-based teaching mode in network teaching.

On Talent Training and Cultural Development of Saxophone under Network Technology

Saxophone as a kind of musical instrument is unique in shape, playing method, playing skill, timbre and keeping way. The continuous development of network technology provides more possibilities for talent training, and popularizes Saxophone teaching, which can increase the choice of musical instruments for music lovers. Saxophone can further strengthen the performance of ensemble in group performance. This paper studies the relationship between talent training and cultural development of saxophone under the background of network technology, and deeply studies its important value in cultural shaping. After that, it explores how to popularize Saxophone more effectively and how to promote cultural production.