Development Of Internet Research Articles

Strategies for the Future Development of Television Advertising

This study examines the current status and future development strategies of television advertising regarding economic development. Firstly, it points out that the rapid development of the mobile Internet has profoundly impacted the decline in audience demographics, competitiveness, changes in ratings, and economic revenues of traditional television advertising. It then analyses the impact on the TV advertising economy of the changing social environment, the extensive use and updating of media technologies, the preferences of the millennial generation, and the rising costs and declining effectiveness of advertising. Finally, it proposes development strategies that should actively utilise the unique characteristics of TV advertising, expand advertising channels, reduce costs, and innovate content and formats. This study comprehensively analyses the challenges faced by TV advertising. It proposes strategies to address them, which are of great reference value for understanding the current development of the TV advertising market and formulating future development strategies.

Implementasi Sistem Keamanan Kendaraan Bermotor Menggunakan GPS Berbasis IoT

<p><em>Rapid technological advancements have driven the development of Internet of Things (IoT)-based products, enabling communication between devices via the internet network. Currently, motor vehicle security system is typically performed in a manual manner, such as through the use of steering locks, chains, or wheel locks. This research aims to design and implement an IoT-based vehicle security system that can be controlled automatically. The system integrates hardware components, such as NodeMCU, relays, GPS modules, and fingerprint sensors, with software including Arduino sketch and the Blynk application. The test results show that the system can generate key data, including control range, follow-up to disconnect the vehicle's electrical system, and vehicle location tracking in the form of coordinates accessible through the Maps application. The research resulted in a security system that can be accessed through two methods: close-range using a fingerprint sensor as a replacement for traditional keys, and remotely through the Blynk application connected to IoT technology.</em></p><p> </p><p> </p>

A data sharing scheme based on blockchain for privacy protection certification of Internet of Vehicles

With the vigorous development of Internet of Vehicles (IoV) technology, modern cars equipped with advanced on-board systems are continuously generating massive amounts of data. Utilizing this data can improve driving safety and achieve better service quality in smart transportation systems. Therefore, ensuring the efficiency and security of data sharing is an important issue. Integrating IoV and blockchain technology can provide solutions to the data sharing security problems. This paper researches on IoV data sharing based on blockchain technology. In view of the problem that Internet of Vehicles data is susceptible to denial of service attacks, central failures and privacy leaks, we propose a data sharing scheme based on blockchain for privacy protection certification of Internet of Vehicles. Firstly, a decentralized privacy protection authentication framework is proposed is based on blockchain. Authenticated communication is performed between vehicle nodes and roadside units (as trusted authorities) by using authentication and access authentication schemes. Secondly, the trusted cluster head selected through the weight indicator is responsible for forwarding the information to the Trust Authority (TA), which then forwards the data to cloud storage and records the certificate and hash value on the distributed blockchain, along with other related information. In addition, the solution also uses a practical Byzantine fault-tolerant consensus algorithm to ensure the security and reliability of the blockchain, as well as the efficiency and decentralization of cloud storage. Finally, the TA revokes the certificate of the malicious vehicle node and clears it from the blockchain. Security analysis experiments show that our solution can effectively resist various threats such as counterfeiting, replay attacks, forgery and data tampering, thereby ensuring the security of Internet of Vehicles data sharing. Compared to the proposed solution, our performance has improved by 50.12%, 41.62%, 6.01%, and 29.11%, respectively.

Adaptive Hybrid Optimized LSTM Models: A Novel Computational Framework for Algorithmic Financial Trading System

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years. This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions. The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem like a balanced trading algorithm that are robust and profitable.

A Multi-scale Patch Mixer Network for Time Series Anomaly Detection

With the development of Internet of Things (IoT) technology, a large amount of data with temporal characteristics is collected and stored. How to efficiently and accurately identify anomalies from these data is a major challenge. At present, there are many problems in the application of anomaly detection, including non-stationary data, complex and difficult-to-collect anomalies, the need for real-time detection and the limitation of computing resources. But few methods can comprehensively consider these issues. To overcome these challenges, we propose a lightweight neural network, Multi-scale Patch Mixer Network (MP-MixerNet). It is mainly composed of a Mixer Block based on fully connected layer design, which contains a Temporal-Mixer and a Spatial-Mixer, and can simultaneously model the intra- and inter-series dependencies of multivariate time series. We also perform multi-scale patch segmentation based on frequency analysis, which helps the model extract robust features from multiple period views. In addition, we design an Input Stabilization module to help the model deal with data distribution shift. Experimental results on a public time series anomaly detection dataset show that we are able to achieve higher comprehensive performance with fewer parameters and inference time.

Digital financial inclusion and SME market dynamics: an empirical analysis based on big data of business registrations in China

ABSTRACT With the deepening of internet development, digital financial inclusion has gradually become a crucial force in promoting the market entry of small and micro enterprises (SMEs). This study analyzes the impact of digital financial inclusion on SME market dynamics, using big data of business registrations and digital financial inclusion data of 268 cities in China from 2011 to 2017. Employing two-way fixed effects and instrumental variable regression methods, we find that digital financial inclusion significantly facilitates SME market entrance and reduces exiting risk. Moreover, the level of internet development moderates the impact of digital financial inclusion, with regions having high internet coverage showing a stronger effect in promoting entry; while regions with low internet coverage exhibit a more pronounced effect in reducing exiting. Further heterogeneity analysis suggests that the impact of digital financial inclusion primarily reveals with the breadth of coverage and depth of use. The effects are more significant in regions with high levels of innovation and foreign investment. This paper provides new empirical evidence on the role of digital financial inclusion in SME development and proposes relevant policy recommendations to promote sustainable development for SMEs.

MULTI-VARIANCE ANALYSIS OF CONTENT MARKETING EFFECTS ON CUSTOMER ENGAGEMENT FROM PT WOW INSTAGRAM HALAL COSMETICS

The growth of the global cosmetic industry, including in Indonesia, is supported by technology and internet development, with Instagram content marketing playing a key role in shaping positive brand meaning through customer engagement. This study aims to analyse the role of content marketing aspects on customer engagement. This research used a quantitative approach based on data collected through PT WOW's Instagram posts. MANOVA is used to analyse the influence of several independent variables (content marketing aspects) on more than one dependent variable (number of likes and comments) simultaneously. A total of 1,119 posts were collected over the last 20 months, recorded based on the number of likes, comments, and content. The results of this study state that uploads containing the call to win, the call to act, and the pure question will generate higher customer engagement. Uploads containing benefit-centric content can also create higher customer engagement. Companies can use the managerial implications of this research to select content to be uploaded and aim to attract consumers and increase consumer engagement.JEL: M20, M31.

Research on the Information Interaction Design of Special Agricultural Products Apps from the Perspective of Rural Revitalization

Driven by the Rural Revitalization Strategy, the promotion and sale of special agricultural products have become an important means to enhance the rural economy. With the development of mobile internet, e-commerce platforms based on mobile apps provide new channels for the sale of these products. However, market research reveals that many platforms for special agricultural products face issues related to poor user experience and low user engagement in their information interaction design. Therefore, this paper, from the perspective of information interaction design, identifies pain points in existing app designs through literature review and case analysis, and explores methods to optimize the user experience of agricultural product apps. We gathered user needs and feedback, built user personas, designed interactive prototypes, and validated their effectiveness in terms of user experience, operational convenience, and information transmission efficiency through usability testing. The research results show that the prototype significantly improved information hierarchy, simplified interaction processes, and better met users' personalized needs. The information interaction design of special agricultural product apps should not only focus on functional requirements but also emphasize the emotional experience and cultural identity of users. Visual design should incorporate elements of rural cultural characteristics to enhance users' sense of identification and engagement. Future designs should emphasize the integration of interaction design with rural culture, further improving user experience through innovative information presentation methods and personalized services, thereby supporting the Rural Revitalization Strategy and the sustainable development of agricultural e-commerce.

Research and Analysis on the Application of Radio Frequency Identification Technology in the Internet of Things and Future Development

In the past five years, 5G technology has been widely used, with its development and popularity. "Internet of Everything", that is, through the 5G technology will be all aspects of life together, this concept gradually into the people's field of vision, in a series of 5G-related technology, radio frequency identification technology as one of the important thing Internet technology has received widespread attention. This article will be based on a combination of a literature review and case study method, a brief introduction to RFID technology and its application in the Internet of Things in the current situation and future development of the analysis. This paper provides some inspirations and suggestions for the practitioners in the field of Internet of Things (IoT) through in-depth discussion of the application of RFID technology in IoT; by analysing the application cases and challenges of RFID technology, it can provide important references for the promotion of intelligent development of Internet of Things (IoT).

How does the internet affect regional sustainable development? Fresh evidence from China

While the internet can drive economic development, social progress, and environmental improvement, its influence on sustainable regional development remains unclear. The paper constructed a more comprehensive synthesized assessment indicator system for regional sustainable and internet development, respectively. By incorporating internet-relevant elements into the research framework of regional sustainable, we examined the influence of the internet on regional sustainable development and its mechanism of action. Research has found that the internet has had a positive effect on regional sustainable in China. The heterogeneity analysis revealed that internet infrastructure, internet information resources, and the internet construction environment have had a positive effect on regional sustainable in China. Furthermore, the transmission mechanism analysis suggested that the internet can positively contribute to China’s sustainable development by promoting economic growth, industrial upgrading, and financial development. This study provides new ideas for fully understanding the impact of internet development, especially on regional sustainable development, and is a useful reference for policymakers on the internet and regional sustainable development.

An Audio Encryption Algorithm Based on 2D Chaotic Map and Dynamic S-Box

With the development of Internet, the security of information transmission has drawn widespread attention. In order to enhance the security of audio data communication, we propose an audio encryption algorithm using a two-dimensional chaotic map and dynamic S-box. To address the drawbacks of single control parameters and small chaotic space in one-dimensional chaotic map, we integrate Sine map and Fuchunao map to generate a new two-dimensional chaotic map 2D-SIFN. This map has two control parameters and a larger chaotic space. Then we generate chaotic sequences using the 2D-SIFN, construct dynamic S-boxes using these sequences for permutation operations, and finally apply chaotic sequences for diffusion operations to confuse audio data. The simulation experiment results show that the proposed algorithm performs excellently in metrics such as Number of Sample Change Rate (NSCR) and Normalized Cross-correlation (NCC) analysis, proving the feasibility of applying S-boxes to audio data encryption algorithms and proposing a feasible new approach in the field of secure communication.

The Impact of Digital Transformation on Customer Experience: A Study of How Firms Use Digital to Enhance CX

This pandemic harmed all economies, developed and developing, because the entire world was under lockdown. The corporate community is also experiencing instability as a result of this outcry. Numerous people have experienced job loss for a variety of causes. It was hard for the business organisations to continue because the market and all of the operations were reduced or stopped. Business organisations really needed to change their strategies because the ones they had been using to succeed were no longer workable. Lockdowns, social isolation, and other restrictions made it extremely difficult for businesses to provide customers a genuine sense of their goods or services. Businesses have been forced by COVID-19 to move their product and service promotion and advertising online. in order for them to build a solid clientele even after lockdown scenarios. For corporate companies, virtual buying and an awareness of online customer behaviour are becoming essential. Due to the competitive virtual marketplace, business businesses are prioritising these things, which is the genuine requirement of the current moment. Gradually, regular Internet development and expansion have made eCommerce a part of mainstream corporate operations.

BCBA: An IIoT encrypted traffic classifier based on a serial network model

With the rapid development of the Industrial Internet of Things (IIoT), ensuring the security and privacy of network traffic has become particularly important. Classifying and identifying encrypted traffic is a critical step in enhancing network security, but traditional traffic classification methods often struggle to handle the complexities of the IIoT environment. In this paper, we propose the BCBA model, a bidirectional encoder representation from transformers (BERT)-based serial network model, which significantly improves the encrypted traffic classification performance and can be trained more than four times faster than the original BERT classifier. The BCBA method obtains word vector representations from the embedding layer of the pretrained BERT model, uses convolutional neural networks (CNNs) to extract local features, employs bidirectional long short-term memory (BiLSTM) networks to capture temporal dependencies in traffic data, and leverages a multihead self-attention mechanism to improve global dependency understanding. In experiments on six types of regular encrypted traffic from the ISCXVPN2016 dataset, the BCBA model achieved an F1 score of 99.10%, outperforming traditional traffic classification techniques across multiple performance metrics. This study demonstrates the effectiveness of deep learning in enhancing the security of the IIoT. It also provides new perspectives and technical routes for future research, particularly in encrypted traffic processing and classification applications.

A Big Data-based Approach to Web User Behavior Analysis

In China, the Internet has developed to a relatively mature scale, and Internet applications have gradually transitioned from being singular to diversified. The Internet is changing people's ways of learning, working, and living, and even influencing the progress of the entire society. Against the backdrop of rapid Internet development, we have gradually entered the "big data era." Faced with such a vast amount of data, single-machine statistics have become inadequate. This paper first introduces the background and significance of the research, providing a preliminary description of network traffic from the perspective of network services. It then elaborates on the concepts and classifications of network user behavior, along with key data, mainly introducing analysis methods. The primary method used in this paper is cluster analysis, including distance and similarity coefficients in cluster analysis, with a focus on the main steps and algorithm process of k-means clustering. Finally, the paper conducts user behavior analysis based on the clustering results.

The Relationship between Intensity of Gadget Use and Speech and Language Development in Toddlers

Background : The alpha generation are those born after 2010, who are claimed to be the smartest generation because they were born and grew up in an era of rapid technological and internet development. So that we can find many toddlers who are familiar with gadgets. However, excessive use of gadgets will reduce toddler interaction with the environment and toddlers rarely communicate with people around them, this naturally disrupts the process of toddler speech and language development. Objective: This study aims to analyze the relationship between the intensity of gadget use and the development of speech and language aspects in toddlers in Wameo Village, Baubau City. Method: This study is an observational analytic type with a cross sectional approach. This study used 77 samples taken using purposive sampling technique which were then measured using a questionnaire. Result: The results showed that toddlers who used gadgets with mild intensity experienced normal speech and language development, 10.4% of toddlers who used gadgets with moderate intensity experienced delays, and 33.8% who used gadgets with severe intensity experienced delays. Hypothesis testing using the Chi-Square Test obtained a p-velue value of 0.000 (P <0.05). Conclusion: In conclusion, there is a significant relationship between the intensity of gadget use and delayed speech and language development in toddlers in Wameo Village, Baubau City.

Adaptive scheduling for Internet of Vehicles using deconfounded graph transfer learning

With the rapid development of Internet of Things technology, the Internet of Vehicles has emerged as a significant application. The Internet of Vehicles connects vehicles with traffic infrastructure and other vehicles, enabling real-time information sharing and vehicle dispatching. However, in the Internet of Vehicles environment, traffic scheduling systems face challenges such as sparse raw data and environmental confounding bias, which limit the effectiveness of traditional graph-based reinforcement learning methods. In this paper, we propose a Cross-City Federated Continual Learning Framework for Spatiotemporal Graph Transfer Learning called CCFTL, which removes confounding effects on reinforcement learning and enhances few-shot transfer learning in data-scarce cities. Our approach aims to significantly improve the adaptability and scalability of cross-city vehicle networking technologies in ever-changing environments. This study acts as an innovative research of the deconfounding strategy with spatiotemporal graph meta-knowledge learning, which enables optimization of cross-city meta-knowledge transfer through a federated continual learning approach. This approach effectively reduces learning bias caused by regional differences, which enhances the model’s generalization and adaptability to complex, heterogeneous urban scenarios. Experimental results show that our framework significantly outperforms traditional methods, especially in improving the efficiency and accuracy of traffic dispatch systems in Internet of Vehicles environments with scarce data and the presence of confounding factors.

Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration.

With the rapid development of Industrial Internet of Things (IIoT) technology, various IIoT devices are generating large amounts of industrial sensor data that are spatiotemporally correlated and heterogeneous from multi-source and multi-domain. This poses a challenge to current detection algorithms. Therefore, this paper proposes an improved long short-term memory (LSTM) neural network model based on the genetic algorithm, attention mechanism and edge-cloud collaboration (GA-Att-LSTM) framework is proposed to detect anomalies of IIoT facilities. Firstly, an edge-cloud collaboration framework is established to real-time process a large amount of sensor data at the edge node in real time, which reduces the time of uploading sensor data to the cloud platform. Secondly, to overcome the problem of insufficient attention to important features in the input sequence in traditional LSTM algorithms, we introduce an attention mechanism to adaptively adjust the weights of important features in the model. Meanwhile, a genetic algorithm optimized hyperparameters of the LSTM neural network is proposed to transform anomaly detection into a classification problem and effectively extract the correlation of time-series data, which improves the recognition rate of fault detection. Finally, the proposed method has been evaluated on a publicly available fault database. The results indicate an accuracy of 99.6%, an F1-score of 84.2%, a precision of 89.8%, and a recall of 77.6%, all of which exceed the performance of five traditional machine learning methods.

Optimasi Jaringan Sensor Nirkabel untuk Monitoring Suhu dan Kelembaban

The development of Internet of Things (IoT) technology has provided new opportunities to improve monitoring through the integration of temperature and humidity sensors in wireless networks. This study aims to optimize a wireless sensor network for temperature and humidity monitoring. This study combines an experimental approach with network performance analysis and optimization methods. First, the selection of temperature and humidity sensors is carried out. Next, an efficient and reliable wireless sensor network architecture is designed to collect temperature and humidity data from various locations. The process of sending data from sensors to base stations and integration with IoT platforms are also studied. Furthermore, a series of experiments are conducted to test the performance of the wireless sensor network under real conditions. Analysis of the collected data is used to identify potential improvements in network performance and improve system reliability and responsiveness. The performance parameters evaluated include connection stability, data throughput, energy consumption, and communication latency. The results of this study contribute to the development of a monitoring system that is reliable, efficient, and easy to implement.

Research on the Reform of Wushu Teaching Mode in Colleges and Universities under the Background of internet plus

As an indispensable part of China traditional culture, Wushu is not only an important way of physical exercise, but also embodies the spiritual connotation of Chinese traditional culture. With the rapid development of internet plus , Wushu teaching mode in colleges and universities has met with great development opportunities and challenges. Based on this, this paper first analyzes the significance of reforming the teaching mode of Wushu in colleges and universities under the background of internet plus , and puts forward a series of innovative strategies of Wushu teaching based on the Internet, aiming at providing important reference for promoting the further development of Wushu education in colleges and universities.

Time-encoded photonic quantum states: Generation, processing, and applications

Encoding and processing quantum information in the time-of-arrival of photons offer significant advantages for quantum information science and technology. These advantages include ease of experimental realization, robustness over photon state transmission, and compatibility with existing telecommunication infrastructure. Additionally, time-of-arrival encoding has the potential for high-rate quantum communication and holds promise for the future development of quantum internet. This review explores the generation, processing, and applications of time-encoded quantum states, focusing on both single-photon states, energy–time entanglement, and time-bin entanglement. We summarize the nonlinear optics platforms and advanced laser and modulation techniques utilized for photon sources that enable quantum information encoding onto the photons' time-of-arrival. We also highlight advanced quantum state processing methods in the time domain, including the Franson interferometry, optical switch-based schemes, and state-of-the-art measurement and detection schemes that allow for high-speed and multi-dimensional quantum operations. Finally, we review the mainstream implementations mainly including the quantum communication demonstrations and outline future directions for developing practical quantum networks leveraging time-encoded photon states.