Content Distribution System Research Articles

Application and Development Prospects of AI in News Editing

The application of artificial intelligence (AI) in news editing is becoming increasingly widespread. This paper systematically reviews the current applications of AI in news content generation, data analysis, news prediction, distribution, and recommendation systems. It analyzes its profound impact on the news industry and explores future development trends and industry practice recommendations. By effectively utilizing AI technology, news organizations can significantly improve the efficiency and quality of news production, driving innovation and development in news dissemination. The continuous advancement of AI technology presents unprecedented opportunities for the news industry. This paper aims to provide valuable references for academia and industry to promote the healthy development of AI technology in the field of news communication.

The Influence of TikTok Marketing on Consumer Purchase Intention among Students

TikTok marketing has an enormous and broad effect on the purchasing intentions of students. TikTok has become an extremely effective medium for marketers to connect with consumers due to its interesting and simple-to-read content structure. TikTok's algorithmic content distribution system is designed to draw in users and market a range of goods and services. This study looks into how TikTok marketing affects students' intentions to buy at selected Malaysian higher education institutions. The growing popularity of TikTok as a social media platform, especially among young people, emphasises the need to comprehend its influence on consumer behaviour. In this study, a quantitative methodology was applied, and analytical approaches employed, including reliability analysis and multiple regression analysis. The study concentrated on three important variables that affect consumers' intentions to buy: brand awareness, product quality, and price. A total of 115 respondents from the chosen universities took part in responding to the sent questionnaire. The research aims to offer insights into how businesses may effectively exploit the platform to engage with students as consumers by looking at these aspects in the context of TikTok marketing. The findings show that TikTok marketing has a major impact on student purchase behaviour, with product quality and price having a major influence on intentions to buy. All things considered, this study deepens our knowledge of how TikTok affects consumer behaviour, especially among students. It demonstrates the platform's potential as an ideal instrument for engaging students as customers and provides important insights for businesses trying to maximise their TikTok marketing efforts. Future studies on the evolving landscape of social media marketing and its effects on customer behaviour under various conditions can build on this research.

Intelligent Content Distribution System: A Machine Learning–Based Teaching Content Customization Method

With the rapid development of information technology and the increasing diversity in the education field, personalized teaching has become the key to educational innovation. However, existing methods of customizing and distributing teaching contents often fail to effectively cope with complex teaching environments and resource constraints. To solve this problem, this study proposed a novel intelligent content distribution system, i.e., a teaching content customization method based on machine learning. First, an inverse optimization model of teaching content customization targeted at content combinations was constructed, which minimized the adjustment of configuration parameters and customized resource constraints. This model generated more reasonable and effective teaching content configuration plans by comprehensively considering content diversity, students’ needs, and resource constraints. Second, to further improve the efficiency and effect of content distribution, an intelligent content distribution algorithm based on a hierarchical reinforcement learning network was developed, which allowed the system to automatically learn and adjust content distribution strategies based on students’ needs and preferences, and optimized the teaching content configuration while meeting resource constraints. The method proposed in this study not only significantly improved the teaching effect, but also provided strong technical support for the rational allocation of educational resources, thereby bringing profound impacts to modern education. This study is of great significance for intelligent content distribution and personalized teaching in the education field.

“Springboard” — a Conceptual Leap into Field of Regional Media

The publication reflects on the role of the concept of publication in the conditions of the modern media market. In the conditions of the region, the project should be based on the needs and values of the target audience. The Omsk media resource Tramplin, which was born during the pandemic, is an example of a new conceptual solution. Omsk is known as a city with an “unstable” background mood, with a high migration outflow and a lack of a positive attitude towards the area of residence. In this situation, several enterprising creators decided to offer the market an alternative point of view on the space in which they live. Springboard Media is a resource about the love of the city, encouraging the reader to share this feeling. The concept of the publication resulted in a content strategy, which can be traced at the level of selection of facts, graphic and structural design of the resource. The developed content distribution system found support and response from readers. This is evidenced by the marketing audit of the resource, undertaken during the study, its results are presented in the publication. The emergence of such projects indicates a new stage in the development of regional journalism, which needs to correlate the values broadcast by the media.

Confidentiality in medical images through a genetic-based steganography algorithm in artificial intelligence.

Nowadays, image steganography has an important role in hiding information in advanced applications, such as medical image communication, confidential communication and secret data storing, protection of data alteration, access control system for digital content distribution and media database systems. In these applications, one of the most important aspects is to hide information in a cover image whithout suffering any alteration. Currently, all existing approaches used to hide a secret message in a cover image produce some level of distortion in this image. Although these levels of distortion present acceptable PSNR values, this causes minimal visual degradation that can be detected by steganalysis techniques. In this work, we propose a steganographic method based on a genetic algorithm to improve the PSNR level reduction. To achieve this aim, the proposed algorithm requires a private key composed of two values. The first value serves as a seed to generate the random values required on the genetic algorithm, and the second value represents the sequence of bit locations of the secret medical image within the cover image. At least the seed must be shared by a secure communication channel. The results demonstrate that the proposed method exhibits higher capacity in terms of PNSR level compared with existing works.

Decentralized identifier based illegal digital content distribution tracking system using the format‐preserving encryption algorithm

AbstractThe coronavirus pandemic, which first began in December 2019, has completely changed our daily lives. In particular, daily online digital content consumption over the world has soared since the start of the coronavirus pandemic. For preventing illegal digital content distribution, various technologies have been applied, but current copyright protection technologies are still lacking to provide secure digital content distribution while preserving privacy. In this article, we introduce a new digital content distribution system, which utilizes blockchain‐based decentralized identifier and format‐preserving encryption to enable user privacy preserved digital content tracking. We present the system architecture with detailed message flows of the proposal and provide its analysis result in terms of security and overhead.

The Effectiveness of Subsidies and Taxes in Atomic Congestion Games

Are subsidies or taxes more effective at influencing user behavior? To answer this question, we focus on the well-studied framework of atomic congestion games which model resource allocation problems in non-cooperative environments. Examples of such resource allocation problems include transportation networks, task assignment, content distribution systems, among others. Monetary incentives, in the form of taxes or subsidies, are commonly employed in such systems to influence self-interested behavior and improve system efficiency. Our first result demonstrates that subsidies can provide strong improvement guarantees when compared to taxes of a similar magnitude. While interesting, our second result demonstrates that this improvements come at the expense of robustness. In particular, taxes provide greater robustness guarantees to mischaracterizations in the societal response when compared to subsidies. Hence, whether a system operator should employ taxes or subsidies depends intimately on the knowledge of the user population.

Joint Link Rate Selection and Adaptive Forward Error Correction for High-Rate Wireless Multicast

Multicast communication over wireless networks has many potential applications, such as real-time audiovisual content distribution or digital signage systems with multiple remote terminals. However, today’s common 802.11 networks cannot fully support such applications at the link layer due to the technical challenges in achieving both high transmission rate and high reliability for multicast data transfers. Those challenges primarily emerge from the inapplicability of immediate acknowledgment mechanisms of unicast transmissions to the multicast case and the need for finding a common transmission rate for all receivers with diverse channel conditions. The research literature in this domain mainly addresses the problem at the higher layers of the protocol stack. In this article, we present a novel approach that combines wireless link rate selection with an adaptive packet-level Forward Error Correction (FEC) mechanism in order to achieve high-rate and highly reliable multicast in 802.11 wireless networks. The integration of FEC into the 802.11 MAC layer allows direct interaction between the transmission rate and the coding scheme. As a result, potential packet losses caused by higher link rates can be compensated by the adjustable redundancy provided by FEC. In combination with an aggregated receiver feedback mechanism, this yields improved transmission efficiency and reliability for wireless multicast. We investigate this approach in a simulation environment under a realistic wireless channel model in various application scenarios with up to 50 receivers. The results represent significant performance improvements, in terms of throughput, channel utilization, and packet loss, over the state-of-the-art methods for reliable wireless multicast.

Next-Generation OTT Distribution Architecture Supporting Multicast-Assisted ABR (mABR) and HTTP/3 Over QUIC

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The potential audience for streaming media services is massive, but satisfying that kind of demand poses a huge technical challenge. Even if existing content delivery networks (CDNs) were capable of distributing such loads, the costs at that scale would remain beyond the monetary capabilities of any broadcaster. By reducing transmission costs associated with content delivery at scale, broadcasters would pay less to the CDNs, making the content cheaper to the end user. In traditional broadcasting technologies, a majority of commercially available content distribution architectures are based on a paradigm rooted in linear broadcast services, having a very basic media service composition, solely focusing on the delivery of content interpretable on a vast amount of compatible TV sets with built-in tuners.While this helped transform the industry by facilitating the transition from analogue to digital broadcast content distribution, it now leaves content providers and broadcast network operators wondering how they can remain relevant in the age of web and internet streaming. This article will take its point of departure in evolved content distribution systems and the resulting system architectures for next-generation broadcast over-the-top (OTT) live and on-demand streaming. It will focus on a solution for efficient and secure transport of well-known low-latency streaming protocols by employing QUIC as a primary transport protocol for the industry leading IP multicast architectures supporting multicast-assisted adaptive bitrate (mABR) streaming, enabled by novel HTTP/3 server push techniques. At scale, the solution could significantly reduce the amount of open streaming sessions, improving the scalability of existing audio–video streaming architectures, and reducing the wastage of scarce bandwidth. Placing physical gateway boxes within users’ home network that fully support QUIC and multicast content reception can reduce the prevalent lack of user agent support for reception of multicast QUIC content. This new model with the core transport protocol based on QUIC will be the foundation for unified content distribution architectures, spanning internet streaming, IPTV, 5G, and the latest ATSC and DVB physical layers, allowing for an efficient, IP-based delivery of linear content to thin clients.</i>

Survey of Network Coding Based P2P File Sharing in Large Scale Networks

Peer-to-peer (P2P) content distribution and file sharing systems aim to facilitate the dissemination of large files over unreliable networks. Network coding is a transmission technique that has captured the interest of researchers because of its ability to increase throughput and robustness of the network, and decrease the download time. In this survey paper, we extensively summarize, assess, compare, and classify the most recently used techniques to improve P2P content distribution systems performance using network coding. To the best of our knowledge, this survey is the first comprehensive survey that specifically focuses on the performance of network coding based P2P file sharing systems.

Super Generation Network Coding for Peer-to-Peer Content Distribution Networks

In peer-to-peer (P2P) content distribution systems, network coding is known as a helpful method for increasing the content availability, accelerating the download process, and robustness against churn. Originally, dense network coding (DNC) has been proposed and theoretically considered as an optimal solution. However, due to its huge computational overhead, it is not viable for real-world systems. Subsequently, sparse, generations, and overlapped generations network coding schemes are proposed as possible alternatives but at reduced performance compared to that provided by the DNC. Further in this article, an improved applicable network coding scheme for P2P content distribution systems referred to therein as Super Generation Network Coding (SGNC) is proposed. SGNC maximizes the generation size so that it is as close as possible to the optimal size without adding computational overhead. Theoretical analysis and experimental work show that SGNC outperforms classical and all previous coding based schemes for P2P content distribution systems in terms of content availability, download time, overhead, and decodability for all piece scheduling policies.

Implementation of Network-Coded Cooperation for Energy Efficient Content Distribution in 5G Mobile Small Cells

The continuous increase of mobile data traffic calls for the design of energy-efficient content distribution mechanisms, to be incorporated in the fifth generation of mobile networks, 5G. One of the biggest concerns of the companies and the research community is to reduce the energy consumption in both the user equipments (UEs) and the network equipment. In this article, we present a novel content distribution framework called Network-Coded Cooperative (NCC) Networks, which benefits from the interplay between mobile clouds (MC) and Random Linear Network Coding (RLNC) to reduce the overall energy consumption in the devices that take part in the communication. This novel framework leads to reduced energy consumption by offloading the cellular interface to a link with greater energy efficiency, for instance, WiFi, within the mobile small cell. We evaluate the performance of our framework analytically and in practical implementation (i.e., testbed) in terms of throughput, energy savings, packet decoding ratio, latency, and synchronicity. In comparison to the conventional content distribution system, for the case of four users, the analytical model and the testbed implementation show energy savings of more than 12% and 8%, respectively. Furthermore, network usage is reduced, losses are neutralized, and the content is synchronously distributed to all users.

UStash: A Novel Mobile Content Delivery System for Improving User QoE in Public Transport

Mobile data traffic is growing exponentially and it is even more challenging to distribute content efficiently while users are “on the move” such as in public transport. The use of mobile devices for accessing content (e.g., videos) while commuting are both expensive and unreliable, although it is becoming common practice worldwide. Leveraging on the spatial and temporal correlation of content popularity and users’ diverse network connectivity, we propose a novel content distribution system, uStash, which guarantees better QoE with regards to access delays and cost of usage. The proposed collaborative download and content stashing schemes provide the uStash provider the flexibility to control the cost of content access via cellular networks. We model the uStash system in a probabilistic framework and thereby analytically derive the optimal portions for collaborative downloading. Then, we validate the proposed models using real-life trace driven simulations. In particular, we use dataset from 22 inter-city buses running on six different routes and from a mobile VoD service provider to show that uStash reduces the cost of monthly cellular data by approximately 50 percent and the expected delay for content access by 60 percent compared to content downloaded via users’ cellular network connections.

An Efficient Content Delivery System for 5G CRAN Employing Realistic Human Mobility

Today’s modern communication technologies such as cloud radio access and software defined networks are key candidate technologies for enabling 5G networks as they incorporate intelligence for data-driven networks. Traditional content caching in the last mile access point has shown a reduction in the core network traffic. However, the radio access network still does not fully leverage such solution. Transmitting duplicate copies of contents to mobile users consumes valuable radio spectrum resources and unnecessary base station energy. To overcome these challenges, we propose huMan mObility-based cOntent Distribution (MOOD) system. MOOD exploits urban scale users’ mobility to allocate radio resources spatially and temporally for content delivery. Our approach uses the broadcast nature of wireless communication to reduce the number of duplicated transmissions of contents in the radio access network for conserving radio resources and energy. Furthermore, a human activity model is presented and statistically analyzed for simulating people daily routines. The proposed approach is evaluated via simulations and compared with a generic broadcast strategy in an actual existing deployment of base stations as well as a smaller cells environment, which is a trending deployment strategy in future 5G networks. MOOD achieves 15.2 and 25.4 percent of performance improvement in the actual and small-cell deployment, respectively.

PCLNC: A low-cost intra-generation network coding strategy for P2P content distribution

It has been proven that network coding can provide significant benefits to P2P networks. In recent years, many schemes have been designed to provide low-cost network coding strategies for P2P content distribution systems. In this paper, firstly, we propose a low-cost intra-generation network coding strategy. By designing the upper triangular encoding matrix, we can reduce the computational complexity of encoding and decoding. Secondly, a linear dependency detection method for encoded blocks is proposed. We use the postponement strategy and the loop self-checking strategy to reduce the probability of receiving linearly dependent encoded blocks. At last, we simulate the distribution system using the above strategies on the simulator and compare it to other systems. From the simulation results, the performance of our system is better than that of the other two systems. Taking the average download time as an example, the average download time of our system is 21.0% and 3.17% lower than other schemes respectively, which effectively improves the distribution efficiency.

Multi-source network-coded D2D cooperative content distribution systems

In this paper, we investigate the information transmission in a typical 5G device-to-device (D2D) communication application scenario, i.e., a content distribution system with a number of information sources intending to broadcast their messages to multiple destinations in the vicinity. Due to the dynamic nature of wireless signal propagation links, it is hard to guarantee a satisfactory performance by direct source-destination transmissions, especially when the system is operating in a reuse-mode. Relays can be introduced to the system to solve this problem. However, adopting the conventional repetition coding at relays inefficiently utilizes the available resources, for the considered multi-source scenario. Therefore, we investigate applying a class of finite-field network codes at the relays, when potentially three types of relays are deployed to assist the information distribution process. We proposed the algorithms to derive the system outage probability and analyze the trade-off between energy efficiency and spectral efficiency. Our analytical and numerical results clearly demonstrate the potential of exploiting network-coded cooperative communications in future 5G D2D systems.

A Study on the Collusion Security of LUT-Based Client-Side Watermark Embedding

Recently, Celik et al. proposed a lookup-table (LUT)-based embedding mechanism for spread-spectrum watermarks, in which the content distribution server sends an encrypted content to the client, then the client uses his or her personalized decryption LUT to decrypt the received content, meanwhile embeds his or her personalized watermark into the content. They also provided a brief analysis on the security of the LUT-based embedding in terms of the collusion attack on watermarked contents. However, we find that the LUT-based embedding is vulnerable to not only the collusion attack on watermarked contents but the collusion attack on decryption LUTs as well, due to the fact all clients share the same long-term encryption LUT. In this paper, we present a theoretical analysis on the collusion security of the LUT-based embedding mechanism. The analysis shows that the collusion attack on decryption LUTs is more effective than the collusion attack on watermarked contents. Based on our analysis, the content distribution system proposed by Celik et al. can only be used for package sale, which limits its applications. In order to extend the applications, we suggest that the encryption and decryption LUTs of the LUT-based client-side embedding should be set as short-term keys instead of long-term keys. Finally, simulations are carried out to illustrate and validate our theoretical analysis.

Design of Game and Digital Contents Distribution System based on Block Chain

Design of Game and Digital Contents Distribution System based on Block Chain

WEB-BASED CAREGIVER RESOURCE PORTAL: THE UCLA IMPROVING CAREGIVING FOR DEMENTIA (I-CARED) PROGRAM

Seven out of ten persons with Alzheimer's disease and other dementias are cared for by family members and friends, most of whom have not received any counseling, training or support to prepare them for the caregiving role. There is evidence that personalized interventions for dementia caregivers can lead to improve caregiver and patient outcomes. The Improving Caregiving for Dementia (I-CareD) project aims to provide caregivers of persons with Alzheimer's and other dementias education, training and support. We developed and launched an electronic content distribution system that allows caregivers to receive personalized information tailored to their learning styles and preferences. Caregiver resources in the form of text, audio/video, and web formats were loaded onto the Web-based Caregiver Resource Portal. Dementia care managers (nurse practitioners) were trained and demonstrated ability to create and deploy new resources, create custom emails for the users and view log of when caregivers log in to view content. 166 dementia caregivers received education materials via the I-CareD Web-based Caregiver Resource Portal from October 01, 2016 to January 01, 2017. Of these 66 (40%) caregivers accessed the portal at least once during this period. We found that caregivers who lived with the person with dementia were more likely to access the caregiver resource portal compared to those who did not live with the care recipient [χ(1)=4.073, p = .044]. Average mini-mental state examination (MMSE) score of care recipients of caregivers who did not access the resource portal (login=0; n=9) was 19.67 compared to 15.83 for care recipients of caregivers who did access the resource portal (login≥1; n=6) [t(13)=1.134, p = .277]. A web-based content distribution system for dementia caregiver resources is more likely to be accessed by caregivers who live with the person with dementia. There is a trend towards increased likelihood of accessing electronic content by caregivers of persons with more advanced dementias. Future investigation will focus on the effects of this personalized electronic resource system on caregiver and care recipient outcomes.

The Digital Afterlives of This Bridge Called My Back : Woman of Color Feminism, Digital Labor, and Networked Pedagogy

This article traces the publication history of the canonical woman of color feminist anthology This Bridge Called My Back through its official and unofficial editions as it has migrated from licensed paper to PDF format. The digital edition that circulated on the social blogging platform Tumblr.com and other informal social networks constitutes a new and important form of versioning that reaches different audiences and opens up new pedagogical opportunities. Though separated by decades, Tumblr and This Bridge both represent vernacular pedagogy networks that value open access and have operated in opposition to hierarchically controlled content distribution and educational systems. Both analog and digital forms of open-access woman of color pedagogy promote the free circulation of knowledge and call attention to the literary and social labor of networked marginalized readers and writers who produce it, at once urging new considerations of academic labor and modeling alternatives to neoliberal university systems.