Development Of The Internet Research Articles

Multi Frame Obscene Video Detection With ViT

With the development of the Internet, people are surrounded by various types of information daily, including obscene videos. The quantity of such videos is increasing daily, making the detection and filtering of this information a crucial step in preventing its spread. However, a significant challenge remains in detecting obscene information in obscure scenarios, like indecent behavior occurring while wearing normal clothing, causing significant negative impacts, such as harmful influence on children. To address this issue, an innovative multi frame obscene video detection base on ViT is proposed by this manuscript per the authors, aiming to automatically detect and filter obscene content in videos. Extensive experiments conducted on the public NPDI dataset demonstrate that this method achieves better results than existing state-of-the-art methods, achieving 96.2%. Additionally, it achieves satisfactory classification accuracy on a dataset of obscure obscene videos.This provides a powerful tool for future video censorship and protects minors and the general public.

Application of Deep Learning in Sentiment Analysis Based on Movie Review Datasets

Abstract. With the rapid development of the Internet and social networks. user-generated content (UGC) has exploded on the Internet as an important form of user feedback, especially movie reviews, which are of great value to the film industry and market analysis, and can help relevant groups understand the reputation and evaluation of movies. As a natural language processing technology, sentiment analysis can effectively identify and extract emotional biases in text, providing a new perspective and method for sentiment analysis from film criticism. The results show that using deep learning can significantly speed up this work and make sentiment analysis more efficient and faster. The purpose of this article is to explore the application and specific process of deep learning in sentiment analysis of film criticism datasets, to find the most appropriate model by comparing and analyzing multiple models, and to present the rating data of some films from the perspective of display technology.

Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings.METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

Smart Shopping Carts in Food Retailing: Innovative Technology and Shopping Experience in Stationary Retail

ABSTRACTIn the development of the Internet of Things, smart shopping carts present a digital innovation that can realize cost‐cutting potential and improve the shopping experience. The impact of smart shopping carts on customers' shopping experience is analyzed in stationary grocery retailing. We explore first whether customers are willing to use these technological innovations, specifically smart shopping carts. Second, the effect of smart shopping carts on customers' shopping experiences is evaluated. For this, we employed a mixed method approach combining a survey study and an interview follow‐up study. A purposive sampling recruited 291 stationary grocery shoppers. A variance‐based structural equation analysis tests the research model for the adoption of smart shopping carts and their relevance to customers' shopping experience. Next, 22 interviews were conducted with grocery shoppers following an intervention approach. Customers are willing to use smart shopping carts when shopping for groceries in stationary retailing. Customers focus on the ease of use and usefulness of the shopping carts, whereas privacy risk and temporary enjoyment are less relevant. However, our empirical results show that affective, social (employee), and physical components predominantly shape the shopping experience compared with the technical component of smart shopping carts. Smart shopping carts similarly affect the cognitive component and the social (customer) component. In contrast to other digital retail innovations, customers seem to value utility of smart shopping carts above else. Stationary retailers can, thus, focus their introduction of smart shopping carts on countering technology anxiety and highlighting customer benefits of shopping comfort and reduced checkout times.

IPv6 Era Routing Protocol Evolution: Challenges and Innovations

The shift from IPv4 to IPv6 has been accelerated by the Internet's explosive growth brought on by the development of linked objects. Reacting to the depletion of IPv4 addresses and to manage the growing number of networked devices, IPv6 was published with enhanced addressing capabilities and design improvements. But the significant challenges in IPv6 implementation have required current routing systems to adapt with IPv6 capabilities. This paper analyzes recent advancements designed to enhance the security, scalability, and efficiency of the protocols as well as the primary challenges to change routing protocols for IPv6. We examine protocols like OSPFv3, EIGRP for IPv6, and BGP-4+ and propose methods to improve protocol approaches to meet problems including growing address space, security, mobility, and routing table management. Furthermore suggested are prospective routes of development for routing technology in an IPv6-dominated Internet environment.

Analysis of the Hyperparameter Selection in Machine Learning

Abstract. A Machine learning is now playing a role in various fields. With the development of the internet, the volume of data is increasing, and the algorithms of machine learning are becoming more complex. Adjusting hyperparameters is a challenge for beginners who are new to machine learning. This research implemented a simple neural network model through code. Moreover, this study has changed some of the most basic parameters of the model and trained different models. This study has used visualization methods to show how the model training time and performance change with the alteration of hyperparameters, and derived their respective accuracy rates. This article provides entry-level data and code for newcomers to the field of machine learning. Since this experiment uses a federated learning model, employs local_update SGD, and also utilizes parallel methods, it is quite comprehensive, covering many algorithm codes for machine learning beginners. Testing the basic parameters of the model can help newcomers to machine learning understand more clearly the impact of hyperparameters on the model. It also aids in the development of more algorithms for adjusting hyperparameters.

A Review of Applications of Deep Learning Techniques in Recommender Systems

Abstract. Nowadays, with tons of information brought by the development of the Internet, recommender systems are becoming more and more popular, and they are affecting many aspects of peoples daily lives. When people use the search engine, shop on e-commerce platforms or watch videos on video software, they are enjoying the content provided by recommender systems. Deep learning techniques also gained a rapid development recently due to their strengths in dealing with complex tasks. Deep learning techniques have triumphed in many areas. Also, there are more efforts contributed to the deep learning based recommender system. However, very few studies focused on giving a detailed review of deep -learning-based recommender system , and these reviews were usually conducted several years ago, which cannot reflect the latest research trends. Hence, the aim of this paper is to summary the deep learning techniques used in recommender systems. A classification scheme is given, and some possible future directions are pointed out.

The Chinese Online Youth Subculture of the "Electronic Wooden Fish" A "Lying Flat" or A "Self-salvation ?"

Abstract: With the rapid development of the Internet, many products have undergone digitalization. The electronic wooden fish, for example, represents a virtual rendition of a religious instrument. In contemporary times, many Chinese youths encounter difficulties in coping with the intense competition prevalent in school entrance examinations and job markets. Also, their limited social recognition inhibits them from fully expressing their opinions in reality. Consequently, resorting to the electronic wooden fish serves as an outlet for these individuals. The youth tend to use the Internet as a means to alleviate stress, thereby giving rise to an online subculture among them. This article delves into the causes and effects of knocking on the virtual wooden fish. By collecting data from three social media platforms in China and conducting thorough analysis, it concludes by identifying three motivations behind knocking on the electronic wooden fish: seeking spiritual merits, relaxation purposes, and sharing experiences with others. These incentives can be explained through various aspects such as cultural symbolism within Chinas context, serving as a tool for decompression, and contributing to self-identity formation among users. Ultimately, this article argues that embracing the subculture surrounding Electronic wooden fish signifies Chinese youths pursuit of self-salvation, rather than lying flat.

A Study of the Blended Teaching Model for Oral English under the Production-Oriented Approach

Abstract: With the rapid development of the Internet, the blended teaching model has gradually become more and more popular. In response to this trend, this paper explores how to improve college students oral English skills under the blended teaching mode of online and offline. Moreover, Considering that nowadays many students can only understand knowledge but find it difficult to apply knowledge, this research will be guided by the Production-Oriented Approach to explore new ideas for improving students' productive ability. The study selects the first chapter of Book 2 of Fluent Oral English Course (Second Edition) as the teaching content and adopts a blended teaching method, which includes online pre-class warm-ups, a combination of online and offline teaching during class, and online homework submission after class. To be mentioned, the teaching process is structured around the principles of motivation, enabling, and assessment. It is hoped that this research will provide valuable references for future studies on the blended teaching model guided by the Production-Oriented Approach and offer inspiration for enhancing oral English instruction.

Management Research on Online Recruitment CompaniesBased on Case Analysis of SEEK

This paper explores the study of management research on online recruitment companies, especially to provide a deeper understanding of the management practices of SEEK, Australias largest online recruitment company, through a case analysis focusing on challenges related to corporate governance, cost of capital, and company structure. With the development of the internet and information technology, online recruitment platforms offer efficient and convenient hiring methods for businesses and job seekers, but they also bring unique management challenges. By analysing SEEKs corporate governance structure, including its board composition and compensation policies, this study reveals key issues online recruitment companies face in governance and capital management. Furthermore, the paper examines SEEKs capital structure and its impact on its operations and growth. Through this case sing of the complexities of managing online recruitment platforms. It highlights both the opportunities created by technological advancements and the governance and financial challenges they face.

Fake Review Detection Model Based on Comment Content and Review Behavior

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants.

“She does a good job for a woman” – the challenges for women enablers in the era of internet histories

This article explores issues of gender imbalance in the historical record of the period of technological development of the internet. It revisits the approach by authors, mostly women, who have criticised the lack of representation of women in the recorded history of technologies. It suggests that women in the period 1980s – 2000s may have been considered as not relevant to the technological record of that period because they were mostly working in invisible support roles, or leading on governance and commercial development matters. Overcoming centuries of discriminatory treatment women enablers have made significant contributions to the growth of new technology organisations but their involvement has been largely hidden from view by gatekeepers seeking to focus on and celebrate the mostly male led engineering and information technology achievements. Illustrated with exemplars from women collected especially for this article, it recommends further study about the roles of the women enablers to complement the already well documented histories in the era of the internet.

A comparative study of various recommendation algorithms based on E-commerce big data

With the rapid development of the Internet and the concomitant exponential growth of information, we have entered an era characterized by information overload. The abundance of data has rendered it increasingly arduous for users to pinpoint specific information they require. However, various forms of recommendation algorithms proffer solutions to this challenge. These algorithms predict items or products that may pique users’ interest based on their historical behavior, preferences, and interests. As one of the current hot research fields, recommendation algorithms are extensively employed across E-commerce platforms, movie streaming services, and various other contexts to cater to the diverse needs of users. In this context, a multi-recommendation algorithms comparison platform is proposed, which includes a two-fold model: online evaluation and offline evaluation. Taking the data set of the Chinese Amazon online shopping mall as the experimental data, item-based collaborative filtering (Item-CF) algorithm, content-based (TF-IDF) algorithm, item2vec model, alternating least squares (ALS) algorithm and neural network algorithm are evaluated in the offline model. In the real-time recommendation part, model-based algorithm is used to achieve the users’ rating mechanism. And the metrics used for evaluation include: precision, recall, accuracy and performance. The experimental results show that the average performance of hybrid algorithms such as ALS algorithm and neural network algorithm is higher than that of other traditional algorithms, and the real-time recommendation system achieves the purpose of improving recommendation speed. By integrating various recommender algorithms into the multi-recommendation algorithms comparison platform, this platform automatically computes and presents various performance indicators based on the user-provided dataset. It aids E-commerce platforms in making informed decisions regarding algorithm selection.

Advanced MiniMax Optimality Strategy in Bandit Problems

Nowadays, with the development of the Internet, recommending personalized information for every user becomes economically valuable. How to properly recommend to users the content they prefer is important. Mostly, bandit strategies are useful for this problem. As one of the most time-saving algorithms, Minimax Optimal Strategy (MOSS) performs well in most data sets. However, the traditional MOSS algorithm applies a conservative strategy in order to minimize the cumulative regrets in the worst situations. This strategy will over-explore the bandit models and spend too much time exploring every arm at the initial stage. This means the MOSS strategy cannot fully take advantage of the known information and then the accuracy and efficiency will be lower. In real life, people have a lot of Internet information to look through and they will not stay on one website or one advertisement for a long time. So, designing an efficient algorithm that performs well in all situations and can make good use of the known knowledge to do quick exploitation becomes so important. Therefore, this research aims to develop an advanced MOSS algorithm that can properly explore each of the bandit arms and improve the performance of the exploitation phase. By achieving this goal, this research adds an enhancement factor on the confidence bound part to speed up the exploration process. The research bases on the MovieLens data set and the data set is divided into 18 categories (arms) according to its genre attribute. Then apply this new advanced MOSS strategy to the designed data set and compare its performance with normal MOSS and UCB algorithms. Results display that the advanced MOSS strategys selection provides higher total rewards and lower average cumulative regrets than others with the advantage of time-saving. This shows the improvement of the advanced MOSS strategy in balancing the exploitation and exploration phases.

An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

Revolutionary discourses from the past: a digital hermeneutical analysis of widely read academic publications on the social impact and significance of the internet

ABSTRACT From the start, the academic community was deeply involved in both the technological and conceptual development of the internet. Works by Rheingold, Castells, and others put forward influential, intellectual imaginaries. Over thirty years later, these sources offer valuable insights, reflecting the novelty and excitement of the time. Often infused with rhetoric of radical transformation, these authors either consciously or unconsciously foregrounded a revolutionary period marked by digital utopianism, while also presenting critical views. This paper aims to identify and historicize common themes and concepts in influential academic publications on the internet's significance and social impact. By adopting a longitudinal and comparative approach, we aim to provide a historically informed understanding of Internet Studies. Our paper contributes to a tradition of media historical scholarship, examining how new technologies were socially constructed, how dominant discourses shaped popular imaginaries, and their role in the evolution of Internet Studies.

High Performance and Colorful Polymer Thermoelectrics with Imprinted Porous Film.

The rapid development of the Internet of Things and wearable electronics has generated growing interest in creating high-performance and visually striking polymer thermoelectric (TE) devices. However, existing polymer TE materials have yet to fully meet these diverse demands. In this study, imprinted porous polymer films are introduced that exhibit both high TE performance and a spectrum of structural colors. The porous architecture not only preserves excellent charge transport properties but also significantly enhances phonon-like scattering, resulting in a more than 50% reduction in thermal conductivity for the PDPPSe-12 film. This leads to a 170% increase in the figure of merit (ZT), achieving a peak value of 0.52 at 363K. Furthermore, the highly ordered porous structure imparts the PDPPSe-12 films with both a wide range of structural colors and remarkable stretchability, making them ideal for wearable TE generators. This approach is widely applicable to various polymeric systems, offering a novel strategy for advancing state-of-the-art plastic TE materials through microstructural engineering.

Douyin—— the leading application in short video in the next decade

In recent years, along with the comprehensive innovation of communication technology, new media has also achieved rapid development and gradually penetrated into various fields. According to the 48th Statistical Report on the Development of the Internet in China released by the China Internet Network Information Center, as of June 2021, the number of Internet users in China has reached 1.011 billion, of which 888 million are short video users, accounting for 87.8% of the total Internet users. According to the survey results of the products most commonly used by short video users from 2020 to 2021, Douyin ranked first with 45.2%. As one of the emerging social media, Douyin short video APP is different from the previous simple graphic social, but the combination of music, special effects and video, so as to closely connect users of different ages, industries and identities. Based on this, this paper will discuss the possibility of Douyin platform to achieve fine operation and further expand market share by optimizing user generated content (UGC) model and analyzing the personality and commonality of audience psychology.

The Role of AI Technology Plays in Chinese Students’ Spoken English Learning

With the popularization and development of the Internet, artificial intelligence as a common tool has been widely used in Chinese students’ spoken English learning. Language learning assisted by AI technology is developed from information technology-assisted language learning. Although it has many advantages, it has not been fully developed and applied, and there is a research gap in the field of technology and ethics. This paper is a systematic review that describes in detail how information technology-assisted language learning has developed step by step from computers to artificial intelligence Apps and even robots, and summarizes the advantages of AI technology in spoken language learning, such as vividness, accuracy, and convenience, as well as the limitations of difficult application, high cost, low error-tolerant rate and neglect of cultural differences. Through literature investigation and comparative analysis, this paper summarizes the relevant suggestions and improvement plans for AI technology-assisted spoken English learning.

The role of big data technology in project schedule management: the case of Amazon

With the rapid development of the Internet, big data technology has received widespread attention in the field of management. Traditional project schedule management methods cannot adapt to the complex and changing environment, and some researchers believe that big data technology has become a key tool to improve and optimize project management. However, there is a lack of mature theoretical systems and practical experience to guide the application of big data technology. Based on the case of Amazon, this paper analyses how the company uses big data technology to optimize project schedule management. It is found that the application of big data technology in project management brings significant benefits. It is able to provide empirical and innovative support for project schedule management. This paper discusses the application of big data technology in project schedule management, highlighting its capability to enable timely adjustments. Real-time monitoring and forecasting facilitate proactive management of project directions and potential risks. Moreover, it leverages real-time data to optimize resource allocation and enhance resource utilization. Furthermore, the paper addresses the challenges and proposes countermeasures associated with big data technology in this context. To tackle the issue of uneven data quality, a robust data collection mechanism should be established.