Internet Boom Research Articles

Macroeconomic Productivity Effects of Artificial Intelligence

Abstract Some observers expect that the current wave of new tools based on artificial intelligence (AI) models, such as the large language models, will have strong effects on labor productivity. I present definitions and classifications that help understanding AI as an economic input. I then review theoretical and empirical arguments about macroeconomic productivity effects of AI and conclude that research has so far found no indication that productivity effects of the diffusion of AI are likely to be higher than those associated with the internet boom around the year 2000. While considerable uncertainty around future effects remains, a recent review and calibration exercise by Acemoglu, D. (2024. The Simple Macroeconomics of AI. Cambridge, MA: National Bureau of Economic Research, Working Paper 32487) suggests that the effects might be a lot lower.

A fair and scalable watermarking scheme for the digital content trading industry

The booming Internet economy and generative artificial intelligence have driven the rapid growth of the digital content trading industry, creating an urgent need for the fair protection of the rights of both buyers and sellers. To meet this need, a technique known as buyer–seller watermarking has emerged. Despite its existence, the majority of existing buyer–seller watermarking schemes adopt the owner-side embedding mode, which results in poor scalability. While a handful of schemes adopt the client-side embedding mode to enhance scalability, they either require the deep involvement of a trusted third party or fall short of ensuring complete fairness due to the unresolved unbinding problem. To address these challenges, this paper proposes a fair and scalable watermarking scheme for digital content transactions based on proxy re-encryption and digital signatures. For one thing, this scheme solves the unbinding problem and ensures complete fair protection of the rights of both buyers and sellers. For another, it adopts the client-side embedding mode and has good scalability. Additionally, it eliminates the need for a trusted third party. Finally, theoretical analysis and experiments demonstrate that the proposed scheme achieves the intended design goals and possesses superior efficiency advantages.

Influence of visual merchandising, creative promotion, and price on purchase decisions in live shopping Shopee

Indonesia's internet boom (212.9 million users) fueled e-commerce growth, with Shopee leading the market (158 million visitors). This study explored factors influencing purchase decisions in Jakarta's Shopee live shopping. Live streams offered product visualization, seller interaction, and promotions. We investigated how visual merchandising, creative promotions, and price impacted buying decisions through a survey of 462 respondents. The research found that creative promotions and price significantly influenced purchases, while visual merchandising had no effect.

An efficient intrusion detection model based on convolutional spiking neural network

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

Booming internet use during pandemic on Indonesian students’ faith: threat and opportunity for sustainable religious education and religious inherency formation

ABSTRACT Focusing on Indonesia’s case, this research discusses how booming internet use impacts students’ faith, functioning as both threat and opportunity concerning students’ religious inherency and sustainable religious education (RE). This qualitative study analyses six public junior high schools and 48 interviewed participants. To reveal various religions’ views, the participants belong to six recognised religions in Indonesia: Islam, Christianity (Protestant), Catholicism, Hinduism, Buddhism, and Confucianism. According to thematic analysis, the findings show that teachers and students face challenges in the early shift to online RE, such as technical problems and learning ineffectiveness. These challenges impede student understanding, study preparation and learning motivation, resulting in decreased social skills, lack of communication, declining faith, and weak religious inherency. However, the internet fostered RE continuity during the pandemic, especially contributing to the time efficiency of RE. Therefore, this research formulates the concept of tri-centred religious education, which integrates the roles of family, school, and society to create the students’ self-control. Religious values and social norms should be connected to strengthen students’ religious inherency.

The Influence of the Instability Factors of Influencer Marketing on Brand Marketing -- Based on the Analysis of Florasis's Influencer Marketing Event

This study explores the impact of the instability of influencer marketing on brand marketing in the context of China's mobile Internet boom. With over 1.08 billion mobile Internet users, social platforms like TikTok have emerged as significant e-commerce channels. Influencer marketing, driven by influencers, plays a crucial role in connecting with targeted consumers. The research focused on the theme "The Influence of the Instability of Influencer Marketing on Brand Marketing," utilizes qualitative methods, particularly the process tracking method in case studies. The chosen case for analysis is the Florasis influencer marketing event. The study combines relevant theories, agenda-setting, opinion leaders, and the SOR model of consumption to investigate instability factors and their impact on brand marketing. Instability factors are classified into subjective factors of the influencer, packaging by the influencers team, and three characteristics of the influencer. The analysis identifies causes and influencing factors contributing to the instability of influencer marketing. The study concludes that the instability of these live streams poses risks and impacts on brand marketing, emphasizing the need for corresponding countermeasures in brand marketing strategies and collaborations with Key Opinion Leaders(KOLs).

Auction-Based Crowdsourced First and Last Mile Logistics

The booming of mobile internet and crowdsourcing technology has offered great opportunities for first and last mile logistics (FLML) service. Unlike the traditional FLML service that separates the parcel collection in the first mile from the parcel delivery in the last mile, a new type of crowdsourced FLML service integrates parcel collection and parcel delivery services as a whole, which can significantly improve the efficiency of FLML service. Briefly, in a crowdsourced FLML service, the platform assigns the customers' triggered pick-up parcels to the couriers who are delivering drop-off parcels in terms of the real-time status of couriers ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , capacity, location, and schedule). Existing works solving the crowdsourced FLML problem only consider the utility maximization for the platform but ignore the incentive to the utilities of couriers. Inspired by this, in this paper, we investigate a novel type of crowdsourced FLML problem, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Auction-based Crowdsourced FLML (ACF)</i> , where the platform assigns the couriers with suitable pick-up parcels based on the preferences of couriers with the goal of maximizing the social welfare of the platform and couriers. To solve the ACF problem, we present a novel auction model named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-attribute Reverse Vickrey (MRV)</i> , where the couriers bid on parcels according to their preferences for parcels. Based on the MRV model, we present three efficient assignment algorithms to assign parcels to couriers. In addition, we give theoretical analysis for our proposed algorithms. Extensive experiments examine the efficiency and effectiveness of our solutions.

The Model of “Internet and Intangible Heritage” Reviving Rural Industry – A Case Study of Chenghe Willow Weaving

Chenghe Willow Weaving, a traditional handicraft art with a long history, is an intangible cultural heritage in Hubei Province. It forms glorious cultural deposit and has great artistic value. Now, the continuation of Chenghe Willow Weaving not only injects new vitality into the local rural economy but also helps rural revitalization to a certain extent. However, it faces challenges such as difficulties in inheriting and promoting of the products, insufficient industrialization, poor ability in entrepreneurs’ innovation. Based on the booming Internet economy, after conducting an in-depth analysis of the developing state of Chenghe Willow Weaving and its bottlenecks, this article proposes a series of practical suggestions. Under the model of the combination between the Internet and the intangible heritage, it aims to promote the inheritance and innovation of Chenghe Willow Weaving. Specifically, it includes establishing professional production management team and subdivided production team, enriching brand culture, full use of Internet. It is believed that the industrial development of Chenghe Willow Weaving can be effectively promoted, achieving sustained prosperity of the rural economy. Also it is hoped that this will give some reference for other traditional handicraft industries.

Analysis of Service Quality Improvement Strategies for Internet Online Education Products

In today's booming Internet economy, the Internet online education content, because of its simple content composition and low market entry barriers, so that many companies have chosen this type of business model. In the process of profitability, most companies ignore customer satisfaction and use short-sighted management methods to strive for quick short-term profits, which makes it difficult for the company to carry out long-term operations and retain good teachers and customers. In this paper, the service quality of Internet online education products is discussed as the main content, starting from the content, community-based service quality and after-sales service, analyzing the status quo and making suggestions. In the end, through case study and argumentation, it is concluded that the company needs to improve the diversity of the company's products, improve the quality of community service, as well as build up the specific after-sales service standards of online education products and the division of responsibilities, so that the quality of the Internet online education product service can be effectively improved.

Understanding the Impact of Environmental Conditions on Zero-Power Internet of Things: An Experimental Evaluation

The booming Internet of Things is transforming all industries, making them more productive and efficient. Unfortunately, with its widespread use, a series of challenges are posed by sustainable sensing, harsh working environments, laboursome battery replacement, and the requirement for an ultra-small form factor. The concept of zero-power Internet of Things has recently been proposed to overcome these challenges by integrating farfield wireless power transfer, ambient energy harvesting, and backscatter wireless communications into networked embedded systems. However, the development of far-field wireless power transfer is still in its infancy, and how environmental conditions affect its performance in the real world remains unclear. This article fills this gap and provides an extensive experimental study to quantify the impact of environmental conditions, namely, lineof- sight propagation, multipath interference, building materials, and non-air mediums on far-field wireless power transfer. Inspired by this study, we further shed light on some future research directions.

Deep Graph Embedding for IoT Botnet Traffic Detection

Botnet attacks have mainly targeted computers in the past, which is a fundamental cybersecurity problem. Due to the booming of Internet of things (IoT) devices, an increasing number of botnet attacks are now targeting IoT devices. Researchers have proposed several mechanisms to avoid botnet attacks, such as identification by communication patterns or network topology and defence by DNS blacklisting. A popular direction for botnet detection currently relies on the specific topological characteristics of botnets and uses machine learning models. However, it relies on network experts’ domain knowledge for feature engineering. Recently, neural networks have shown the capability of representation learning. This paper proposes a new approach to extracting graph features via graph neural networks. To capture the particular topology of the botnet, we transform the network traffic into graphs and train a graph neural network to extract features. In our evaluations, we use graph embedding features to train six machine learning models and compare them with the performance of traditional graph features in identifying botnet nodes. The experimental results show that botnet traffic detection is still challenging even with neural networks. We should consider the impact of data, features, and algorithms for an accurate and robust solution.

Achieving high contact-electrification charge density on inorganic materials

Triboelectric nanogenerator has been considered as a potential choice for the booming Internet of Things as distributed energy supply unit. However, the widely used organic materials are restricted by weak aging/temperature resistance in severe conditions. In comparison, inorganic materials possess better aging/temperature resistance, but they have been overshadowed by their inferior charge density. To address this issue, inorganic single crystals have been used as a friction layer to improve the contact electrification effect of inorganic materials. This approach enhances the charge density to 170 μC m−2 and 335 μC m−2, reaching its theoretical value in air and under vacuum, respectively. Meanwhile, a quantitative understanding of the charge transfer mechanism between single crystal and metal is established, which is strongly influenced by the work function and atom species at the interface. This work provides theoretical guidance for the contact electrification process of inorganic material, and breaks its shackle of inferior charge density, greatly expanding the triboelectric series and laying a foundation for the application of triboelectric nanogenerator in extreme environments.

Building a bridge to the future: Prospective legitimation in nascent markets

AbstractResearch SummaryHow do new things in nascent markets become legitimate? Existing research points to a process where legitimacy is built by making associations with already legitimate ideas from other domains. In this study, however, we investigate the Internet boom of the 1990s, a nascent setting where something new—engagement metrics used to evaluate firms—gained legitimacy amongst investors, but not by being associated with already legitimate metrics. Using a question‐driven mixed‐methods approach, we reveal that these new metrics instead gained legitimacy through a novel process we term prospective legitimation, where a new basis of legitimacy was constructed by firms linking their otherwise unproven new metrics to future profitability. We discuss how these findings inform research on legitimacy, the development of nascent markets, and future‐oriented communications.Managerial SummaryFirms in nascent markets often face the challenge of convincing investors to buy into something new. This is difficult because new ideas not only have few precedents, but they also have not been around long enough to have proven their value. Our research shows how firms can legitimate their new ideas prospectively by using future‐oriented communications that link their otherwise unproven new ideas to a desirable future outcome. Through an investigation of the Internet boom of the 1990s, we demonstrate that Internet firms gradually convinced investors to accept their new engagement metrics (e.g., traffic, visitors, users) before there was any concrete evidence that such metrics actually led to profitability. This study thus enhances our understanding of how new ideas gain traction in nascent markets.

Study on the Willingness to Continue Using Online Learning Behaviour in the Platform Economy

With the booming Internet economy, there are an increasing number of online learning platforms, and the number of users using these platforms for learning is growing every year. However, less than 30% of users are actively using these platforms, so it is important to understand users' willingness to use them continuously. In order to investigate the causes of users' low activity on online learning platforms, this paper develops a theoretical model on the factors influencing users' willingness to use these platforms consistently with the aid of expectation confirmation theory and self-determination theory.

Tackling the Fake News Epidemic using Machine Learning Algorithm

Abstract: Fake news has been a problem since the internet boom. Websites that keep us up to date with what's going on in the world are the perfect breeding ground for bad news and fake news. Fighting fake news is important because the world is knowledge-based. People do not make important decisions based on information; they also form their own ideas. Incorrect information can cause serious damage. It is not possible to identify all messages from a contact. This article attempts to speed up the fake news detection process by recommending a reliable fake news classification method. Machine learning contains different algorithms like naive Bayes, passive-aggressive classifiers and deep neural networks used eight different datasets from different sources. The text also includes the analysis and results of each model. With the right standards and the right tools, the task of detecting fake news will not be trivial.

Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph

Due to the boom of Internet of Things (IoT) in recent years, various IoT devices are connected to the Internet and communicate with each other through network protocols such as the Constrained Application Protocol (CoAP). These protocols are typically defined and described in specification documents, such as Request for Comments (RFC), which are written in natural or semi-formal languages. Since developers largely follow the specification documents when implementing web protocols, they have become the de facto protocol specifications. Therefore, it must be ensured that the descriptions in them are consistent to avoid technological issues, incompatibility, security risks, or even legal concerns. In this work, we propose Neural RFC Knowledge Graph (NRFCKG), a neural network-generated knowledge graph based contradictions detection tool for IoT protocol specification documents. Our approach can automatically parse the specification documents and construct knowledge graphs from them through entity extraction, relation extraction, and rule extraction with large language models. It then conducts an intra-entity and inter-entity contradiction detection over the generated knowledge graph. We implement NRFCKG and apply it to the most extensively used messaging protocols in IoT, including the main RFC (RFC7252) of CoAP, the specification document of MQTT, and the specification document of AMQP. Our evaluation shows that NRFCKG generalizes well to other specification documents and it manages to detect contradictions from these IoT protocol specification documents.

Japanese animation development and market strategy

This article will first briefly describe the development process of Japanese animation, and outline that the Japanese animation industry struggled to survive in the Japanese market in the early days, gradually developed and seized market share in the mainstream European and American animation at that time, and successfully seized the international market share. After several difficulties and crises, it finally became the history of Japan's proud cultural soft power. After that, this research will analyze the impact of streaming media platforms on the Japanese animation market in the current booming Internet era, and briefly talk about the opportunities and challenges he faces. At the same time, some measures and changes of the Japanese animation industry in response to the new media crisis will be listed. Finally, it analyzes how the Japanese animation market grows gradually in Japan's domestic market and international market, and analyzes the strategy in the face of changes.

A Learning-Based Context-Aware Quality Test System in B5G-Aided Advanced Manufacturing

The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

Cellulose‐Derived Wearable Carbon Nanoflake Sensors Customized by Semiconductor Laser Photochemistry

AbstractMulti‐functional wearable electrical materials have been regarded as one of the most pivotal cornerstones for the booming internet of things (IoTs), biomimetic robotics/science, and sensory e‐skins. Nevertheless, customizable, high‐throughput, batch‐fabricated, function‐integrated wearable electronics remain technologically challenging to traditional material engineering. Hereby, a cellulose‐converted active amorphous carbon nanomaterial is developed via a transfer‐free, precursor‐free rapid laser synthesis method incorporating deformation‐tolerant waste papers. The lattice fringe spacing of laser‐synthesized carbon nanoflake is ≈0.305 nm topologically distinct from graphene or carbon dots. The nanostructured three‐dimensional (3D) carbon network exhibits desirable mechanical flexibility, high hygroscopicity/electrical conductivity, large ion storing capacity for Zn2+ or Na+, high sensitivity to pressure, and a natural microwave absorbing ratio (&gt; 37 dB at the terahertz range). Abundant percolation pathways inside cellulose/carbon composite networks offered fast electrolyte diffusion and carrier mobility. A series of low‐cost highly‐deformable interdigitated supercapacitors, tactile sensors, electrical circuits, and functional coatings are experimentally fabricated and identified, enabling waste paper as a function‐magnified meta platform for e‐skins, wearable energy devices, or IoTs interfaces.

Penyuluhan Merangsang Jiwa Kewirausahaan Kelompok Produktif Untuk Melahirkan Usahawan Muda Mandiri Di Kalangan Pemuda Dan Pemudi RW 023, Kelurahan Teluk Pucung , Kecamatan Bekasi Utara, Kota Bekasi

It has been almost two years since the COVID-19 pandemic has been going on and during thattime this pandemic has changed the perspective and pattern of human life, including most Indonesianfamilies. After the period starts to improve, this is the time for youth to be so important in handlingCovid-19.Millennials are a generation that grew up in the booming internet era where they have differentcharacteristics of each individual, it depends on where he grew up, his family's economic and socialstrata, his communication patterns are very open compared to previous generations, fanatical socialmedia users and their lives. heavily influenced by technological developments. Likewise with theeconomy, which is increasingly competitive, requires the younger generation to be more creative anddynamic in increasing the value of local products. With the technological capabilities they have, theyounger generation can take advantage of these technological capabilities to increase effectiveness andefficiency in the management of MSMEs which have been managed traditionally and conventionally.MSMEs in the digital economy, from a total of 65.47 million MSME units, the Ministry ofCooperatives and SMEs noted that 17.25 million MSME actors were connected to the digital economyecosystem in February 2022 or around 26.4 percent.