Network Era Research Articles

Consumer Behavior under Social Network Platform Influencing Factors and Marketing Strategies

With the rapid development of modern technology and information technology, society has entered into a new social network era, which also makes the new social network marketing mode gradually replace the original traditional marketing mode, and has even become a social way of the masses at this stage. Therefore, in order to enhance the competitiveness and competitive advantage of enterprises in this diversified market competition environment, it is necessary to deeply explore the marketing characteristics, marketing strategies and marketing models in the social network era to provide a key boost to the sustainable development of enterprises. Therefore, the article analyzes the factors influencing consumer behavior under social networks and proposes marketing strategies for enterprises based on the influencing factors.

Research on the Strategy of Improving the Effectiveness of College Students’ Legal Education in the Information Age -Taking China as an example

With the continuous improvement of the level of information, we have entered the network era. In this paper, the legal education of college students this problem, combined with the characteristics of the Internet age and the status of the rule of law education of college students, analysis of the current network of college students the problems of the rule of law education, indicating the necessity and enthusiasm of the rule of law education of college students network, studied the reform of legal education of college students in the Internet age.

Reconsidering the Network Era: 16mm Film on Television

Reconsidering the Network Era: 16mm Film on Television

Innovative Development of Enterprise Value Network under Internet Economy

China's social economy is under the general trend of continuous progress and development, which makes the competition between enterprises increasingly fierce. Under the influence of the network era, the forms and means of competition among enterprises in China have changed greatly. Therefore, when improving their competitiveness, it is necessary to gradually shift the focus to the direction of network innovation, so as to show their comprehensive strength through the confrontation of network strategies. This paper analyzes the importance of enterprise value network, and then expounds its advantages. Then it explores the innovation architecture of enterprise value network under the Internet economy, and finally puts forward several practical innovation strategies.

HERTA HERZOG’S RESEARCH - THE FORERUNNER OF THE USES AND GRATIFICATIONS THEORY

The uses and gratifications theory represents one of the key theories that contributed to the change of the theoretical paradigm regarding the maximum effects of the media on people. The theoretical question “What do the media do to people?” within this theory has been replaced by the question “What do people do with the media?”. Along with this theory, the concept of an “active audience” was also introduced. Specifically due to the interaction that they enable users to achieve, the uses and gratifications theory itself, as well as the concept of an active audience, have been reinterpreted in the era of digital media and social networks. In view of the aforementioned, the author believes that it is important to take a look back in time, particularly to the 1940s and highlight Herta Herzog’s groundbreaking research on the active role of media audiences, which were highly important to the team gathered around Paul Lazarsfeld and Elihu Katz when laying the foundations of the uses and gratifications theory.

Motives and antecedents affecting green purchase intention: Implications for green economic recovery

Green consumption is not only a component but also a key to recovering the green economy. The present study developed a theoretical model to understand better the antecedents of consumers’ green purchase intentions in the social network era. Based on the theory of planned behavior, this study primarily focused on the impact of green consumption characteristics represented by environmental and health factors. Additionally, the influences of word-of-mouth and social factors were also considered. Data were collected from 709 valid structured questionnaires and analyzed by partial least squares structural equation model. The results confirmed the applicability of the theory of planned behavior in predicting green purchase intention. Notably, the findings reveal that environmental concern, environmental knowledge, health status, health consciousness, self-identity, and word-of-mouth have significant direct or indirect effects on green purchase intention. In summary, this study contributes to the researcher’s knowledge of green consumption, highlights the crucial role of green consumption characteristics, and provides practical policy implications for green recovery.

Exploring the Influence Mechanism of Chinese Young Researchers' Academic Information Avoidance Behavior

Researchers can obtain and use information from different channels in the network era, and the explosive growth of information may lead to researchers' academic information avoidance behavior. However, the influence mechanisms of academic information avoidance behavior are unclear. Two studies were conducted to address this issue. In Study 1, we chose young researchers in universities as the research objects to summarize influencing factors including academic information overload, information independence, research task difficulty, time pressure, academic information literacy, negative emotions, and cognitive dissonance of academic information avoidance behavior using social cognitive theory (SCT) and grounded theory. In Study 2, we analyzed the different configuration and key characteristics of academic information avoidance behavior for young researchers using the fuzzy set qualitative comparative analysis (fsQCA) method. Our results showed that there were four configurations for the formation of academic information avoidance behavior, which were summarized into three types: “time-driven” with time pressure as the core condition, “information-cognitive-driven” with information independence and cognitive dissonance as the core condition, and “emotion-driven” with negative emotions as the core condition. Our study helps to enrich the theory of academic information avoidance behavior, and provides recommendations for young researchers and university libraries in academic research and information services.

Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach

In today’s era, the importance and implementation of blockchain networks have become feasible as it improves the resilience of the supply chain network at all levels by clarifying information and creating security in the network, improving the speed of response, and gaining the trust of customers. This paper aims to investigate the behavior of the blockchain acceptance rate (BAR) in the home appliances flexible supply chain in Iran using. system dynamics (SD), which is used to better define the relationships between the variables of the model that are non-linearly connected. Through simulating the behavior of the BAR in the long term in the supply chain, whilst conducting sensitivity analysis, policy design, and validation, this model will be implemented for the years 2020 to 2030. Additionally, post-simulation, blockchain acceptance behavior will be assessed by having simulated data considered as input for studied Multi-Layer Perceptron (MLP) and Vector Regression (SVR) (data that have the highest correlation with BAR). The acceptance rate behavior is predicted with the help of machine learning methods to have the best behavior and prediction for the data of 2020-2022 since the prediction function is compared to daily real data obtained these years. The results show that in 2030, the BAR will be around 0.6 if the COVID-19 outbreak impact is medium, and if the considered policy designs are implemented, this rate will reach a maximum of 0.8. So paying attention to the creation and design of policies can achieve positive implications for increasing the resilience of the supply chain in the long run. Findings suggest that the SD-MLP method is better than the SD-SVR method as it has less error and can predict the better behavior of the BAR.

Neuromorphic Functional Modules of a Spiking Neural Network

In the current era, the design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks (ANN) effectively solve a wide range of common artificial-intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of the behavior of an object. The biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to large parallel and asynchronous data processing. In this paper, we present a circuit design of main functional blocks (neurons and synapses) intended for the hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timed spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approaches to emulating synaptic behavior in hardware implemented spiking neural networks is to use nonvolatile-memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structures to mimic synaptic plasticity, pre- and postsynaptic neurons which could be used for developing a circuit design of spiking-neural-network architectures with different training algorithms including the spike-timing-dependent-plasticity learning rule. Two different circuits of an electrical synapse are developed. The first one is an analog synapse with a photoresistor optocoupler used to ensure the tunable conductivity for synaptic-plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without a digital-to-analog converter and photoresistor optocoupler). The results of prototyping the developed circuits for electronic analogs of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for the ASIC (application-specific integrated circuit) design of spiking neural networks based on CMOS (complementary metal—oxide–semiconductor) design technology.

Blockchain-Based Trusted Traffic Offloading in Space-Air-Ground Integrated Networks (SAGIN): A Federated Reinforcement Learning Approach

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security.

Political Fake News Detection from Different News Source on Social Media using Machine Learning Techniques

People are more dependable on online news systems than ever in this modern time and day. The more people depend on online news, magazines, and journals, the more likely it will have more significant consequences of fake news or rumors. In the era of social networking, it has become a significant problem that negatively influences society. The fact is that the internet has become more accessible than ever, and its uses have increased exponentially. From 2005 to 2020, overall web users have increased from 1.1 billion to 3.96 billion. As most individuals' primary sources are microblogging networks, fake news spreads faster than ever. Thus it has become very complicated to detect fake news over the internet. For that purpose, we have used four traditional machine learning (ML) algorithms and long short-term memory (LSTM) methods. The four traditional methods are as follows logistic regression (LR), decision tree (DT) classification, k-nearest neighbors (KNN) classification, and naive bayes (NB) classification. To conduct this experiment, we first implemented four traditional machine learning methods. Then we trained our dataset with LSTM and Bi-LSTM (bidirectional long-short term memory) to get the best-optimized result. This paper experimented with four traditional methods and two deep learning models to find the best models for detecting fake news. In our research, we can see that, from four traditional methods, logistic regression performs best and generate 96% accuracy, and the Bi-LSTM model can generate 99% accuracy, which outbreaks all previous scores.

Research on Business Model Innovation Based on Big Data Analysis

With the continuous development of information technology, the network era is gradually coming and has an important impact on all industries, which makes the business model of enterprises have to keep pace with the times to achieve innovation, so as to enhance the competitive advantage of enterprises and promote the healthy development of enterprises. Driven by big data, the pace of business model change is faster. All kinds of data are being produced all over the world in an instant, and this momentum is getting stronger and stronger. Big data is an important ignition point to promote the business model to be updated and reformed, and it is also a key factor to promote the increasingly fierce market competition. Therefore, it is particularly important to strengthen the research on business model innovation driven by big data. Based on the background of the Internet age and computer technology, this paper explores the innovative strategy of business model, so as to promote the innovation of business model and the development of enterprises.

Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks.

Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement. More recently, transformer networks have emerged that replace convolutions with a complex attention mechanism, and they have already matched or exceeded the performance of CNNs in many tasks. Transformers need very large amounts of training data, even more than CNNs, but obtaining well-curated labeled data is expensive and difficult. A possible solution to this issue would be transfer learning with pretraining on a self-supervised task using very large amounts of unlabeled medical data. This pretrained network could then be fine-tuned on specific medical imaging tasks with relatively modest data requirements. The authors believe that the availability of a large-scale, three-dimension-capable, and extensively pretrained transformer model would be highly beneficial to the medical imaging and research community. In this article, authors discuss the challenges and obstacles of training a very large medical imaging transformer, including data needs, biases, training tasks, network architecture, privacy concerns, and computational requirements. The obstacles are substantial but not insurmountable for resourceful collaborative teams that may include academia and information technology industry partners. © RSNA, 2022 Keywords: Computer-aided Diagnosis (CAD), Informatics, Transfer Learning, Convolutional Neural Network (CNN).

What we do in the shadows: The consumption of mobile messaging by social media mobile apps in the twilight of the social networking era

What we do in the shadows: The consumption of mobile messaging by social media mobile apps in the twilight of the social networking era

Emigration to the internet: “Samizdat” and the genesis of contemporary Russian nationalism

During the mid-1990s the Pro-European concept of the Russian nation was formed in the post-Soviet ideological space, contrary to the prevailing traditionalist, soil-based forms of Russian nationalism, mainly focused on the search for a “special way”. The former is based on the values of democracy and individualism while the main emphasis of the ideology is the leadership of intellectuals in political life. This article studies the genesis of aforementioned trend in contemporary Russian nationalism in the context of two historical circumstances: the ideological and social status of post-Soviet intellectuals and the Russian-language Internet at the stage of transition to social networks. A key element of the transition period is the emergence of mass online communication, anticipating the beginning of social networks era. Combination of elitism and emphasis on the text were combined with increasing popularity and anonymity, where all these qualities were embodied in guest books. Thus, we will try to show how the communicative environment of the early Runet was arranged and how it influenced rhetorical strategies, styles of public behavior and ideology of contemporary Russian nationalists. As an example in our work, we will consider Dmitry Galkovsky’s Internet resource “Samizdat”, which was functioning in the late 1990s.

The Defenders’ Abortion Case: Revisiting a Television Controversy

This article examines a 1962 episode of The Defenders as a landmark scripted drama that staged a debate about unplanned pregnancies and abortion access in the early network era. “The Benefactor” served as a test of television networks’ authority, and its success created space for more open discussion of controversial topics in prime time.

Target driven design of electromagnetic metamaterial for dual-band Wi-Fi energy harvester

With the development of Internet of things (IoT), there have a large amount of interconnected devices communicates with each other through electromagnetic (EM) waves sent by Wi-Fi router. It is promising to harvest Wi-Fi energy realizing wireless power transmission for sensors or microelectronic devices. However, the energy is hardly to be harvested effectively due to the complexity and disorder of EM waves. To address such issue, we propose an EM metamaterial based on parametric equation as dual-band Wi-Fi energy harvester operating at 2.45 GHz and 5.8 GHz. By adjusting these equation parameters, the prototype can achieve almost an arbitrary shape, avoiding the problem of local optimal solutions. Through the co-simulation of MATLAB and CST Studio Suite software, we design a genetic algorithm (GA) to optimize the proposed structure so that it can harvest energy at Wi-Fi frequency bands. The optimized metamaterial exhibits electric dipole resonance and magnetic dipole resonance simultaneously at 2.45 GHz and 5.8 GHz, and the maximum harvesting efficiency is 96.9% and 95% at normal incidence, respectively. In particular, the metamaterial has the characteristics of polarization insensitive, ultra-wide band and wide-angle harvesting. It is expected to integrate with small electronic devices to realize the function of remote energy conversion, self-powered, and maintenance-free in the era of advanced wireless networks.

A Wavelet Based Digital Image Watermarking Technique through Encryption with DCT

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. Digital image watermarking is an attractive research area since it protects the multimedia data from unauthorized access. Digital images are widely communicated over the internet. The security of digital images is an essential and challenging task on shared communication channel. In the era of big data and networking, it is necessary to develop a secure and robust digital watermarking scheme with high computational efficiency to protect copyrights of digital works. Digital watermarking is need for the security of digital images to achieve security goals, i.e. confidentiality, integrity and availability. With the use of sophisticated signal/image processing algorithms, manipulations and duplications of audio, images, and videos are much easier. Hence, content authentication through encryption and resistance to general attacks such as noise, compression, and geometric has become an urgent and important issue. Hence in this work a wavelet based digital image watermarking technique through encryption with DCT (Discrete Cosine Transform) is presented that can guarantee robustness of image against salt and pepper attack.

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent.

Risk Constraints in the Context of Big Data and Optimization of a Virtuous Interaction Mechanism for Insurance Management Based on Distorted Risk Metrics.

A relevant definition of big data technology has been released by relevant research institutes in China. Big data technology is defined as follows: big data technology is a digitally important productivity factor in the network era; big data technology is based on network technology, statistical technology, and mathematical technology; big data technology, as a carrier of network technology, should be integrated with various industries and technologies other than contemporary ones in order to achieve better informatization and industrialization. In the context of the new crown epidemic in 2020, China's big data technology continues to grow steadily, with a total value of 39.2 trillion yuan, accounting for 38.6% of GDP. This paper investigates the risk constraint in the context of big data and optimizes the benign interaction mechanism of insurance management based on distorted risk metrics in this context. The current situation of insurance risk is investigated through a profound discussion of theories in this paper and through research methods such as distortion risk metrics and SQL Server database management. It is found that the overall situation of insurance industry development in the era of digital economy is not optimistic and so on. And relevant suggestions are made in the conclusion.