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"Music Computer network resources."
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Ripped : how the wired generation revolutionized music
Tells the story of how the laptop generation created a new grassroots music industry, with the fans and bands rather than the corporations in charge.
Book
Fair rewarding mechanism in music industry using smart contracts on public-permissionless blockchain
We develop smart contracts on public-permissionless blockchain to protect the music industry from the consequences of illegal downloading of copyright-protected music files. We develop a decentralized music file-sharing platform, where music file owners can upload the music files, and music file requesters can download the music files. We also establish a fair rewarding mechanism for the music industry for the benefit of music file owners, where music files can be traded through smart contracts between the music file owners and file requesters. Further, we implement a penalty scheme to avoid malicious and illegal music file resources to be added to the community network. This approach is ideal for addressing the problem of illegal distribution of copyright-protected music files without the consent of the owners, which has negative consequences in the music industry. This model considers a community of people networked together, where music file owners can upload their music files to a community network server on approval of the majority of the community and the file requesters can request a paid download from the community network. Music file uploading and downloading transactions are voted and signed by the online community members. All parties are rewarded with an incentive fee per valid transaction. Transactions are handled through self-executing smart contracts. This model further analyzes the costs incurred in music file uploading and downloading transactions based on the uploading/downloading cost, the transaction fee and the networking cost. Perceiving the significance of the smart contract in the music industry, we determine that the effect of illegal downloading of copyright-protected music files can be reduced. We also emphasize the challenges associated with the adoption of the smart contract. Since blockchain interoperability is yet an issue, migrating applications in a heterogeneous network or between platforms can be costly and tedious, which should be further explored in the future. Adapting the proposed architecture for private-permissioned blockchain (Hyperledger Fabric and Libra by Facebook, R3 Corda) and could be a natural extension of this work.
Journal Article
Database design of regional music characteristic culture resources based on improved neural network in data mining
With the improvement of living standards, Music Appreciation Art has gradually become the pursuit of people. As an important part of music resources, regional music is an indispensable treasure of music appreciation art. Regional music culture with its unique charm is constantly affecting modern people’s music appreciation ability. Fully learning regional music culture is the key to carry forward traditional culture. However, as an important part of the cultural treasure house, regional music characteristic culture resources lack reasonable digital storage. Therefore, reasonable and sufficient mining of regional music characteristic cultural resource data is of great significance to the protection of regional characteristic culture. In this paper, the database of regional culture and music characteristic resources is establishedby data mining technology. At the same time, combined with the improved BP neural network model to classify the regional characteristic music andcultural resources data. A set of database including classification, search, audition and storage is established in order to protect and spread the regional music characteristic cultural resources. Finally, it provides new ideas for cultural heritage and cultural heritage.
Journal Article
How do multimedia and blended learning enhance music elective courses? examining the roles of learning attitudes, styles, and teaching presence
This study aims to explore the possibility of using Internet resources to enhance the educational effect of music elective courses in colleges and universities. Moreover, this study analysed students’ perception of blended learning mode and their continuous learning intention. The study adopted the Technology Acceptance Model (TAM) and the theory of sustained learning intention as theoretical frameworks, combined with factors such as learning attitude and learning style, to explore the impact of these factors on students’ learning outcomes. The study found that learning attitudes and styles are positively correlated with students continuous learning intention via questionnaire surveys and quantitative analysis, supporting the research hypothesis. The research results are of great significance for optimizing the design and teaching methods of music elective courses, providing theoretical support and empirical basis for promoting innovation in music education.
Journal Article
The culture of connectivity : a critical history of social media
This book studies the rise of social media in the first decade of the twenty-first century, up until 2012. It provides both a historical and a critical analysis of the emergence of networking services in the context of a changing ecosystem of connective media. Such history is needed to understand how the intricate constellation of platforms profoundly affects our experience of online sociality. In a short period of time, services like Facebook, YouTube and many others have come to deeply penetrate our daily habits of communication and creative production. While most sites started out as amateur-driven community platforms, half a decade later they have turned into large corporations that do not just facilitate user connectedness, but have become global information and data mining companies extracting and exploiting user connectivity. Offering a dual analytical prism to examine techno-cultural as well as socio-economic aspects of social media, the author dissects five major platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. Each of these microsystems occupies a distinct position in the larger ecosystem of connective media, and yet, their underlying mechanisms for coding interfaces, steering users, filtering content, governance and business models rely on shared ideological principles. Reconstructing the premises on which these platforms are built, this study highlights how norms for online interaction and communication gradually changed. “Sharing,” “friending,” “liking,” “following,” “trending,” and “favoriting” have come to denote online practices imbued with specific technological and economic meanings. This process of normalization is part of a larger political and ideological battle over information control in an online world where everything is bound to become “social.”
eBook
Analysis for Online Music Education Under Internet and Big Data Environment
Online music teaching has brought great challenges to traditional music education. This paper investigates the paradigm of online music education and uses neural networks to evaluate its teaching quality based on the internet and big data. The main work is as follows: 1) The research progress of online music education at home and abroad is introduced. 2) This paper introduces the basic principle and algorithm steps of BPNN and establishes the evaluation index system of online music education quality. The experimental results show that the model proposed in this paper has high accuracy in evaluating online music teaching standards.
Journal Article
Research on Online Vocal Music Smart Classroom-Assisted Teaching Based on Wireless Network Combined With Artificial Intelligence
With the rapid advancement of information technology, online education based on big data and artificial intelligence is a hot research topic in education. This study focuses on applying big data and AI in online vocal wisdom classes to enhance personalized teaching and effectiveness. It aims to address issues in traditional vocal education like lack of personalized guidance and poor outcomes. The research proposes an intelligent learning system for vocal education, featuring modules for data collection, analysis, and intelligent recommendations. Functions include personalized learning paths, teaching assistance, real-time feedback, and assessment. Experiments show significant improvements in student outcomes and satisfaction. This innovative approach contributes to enhancing vocal education through personalized and intelligent teaching, offering valuable insights for future education development. Online vocal wisdom classrooms leveraging big data and AI represent a crucial direction with broad application prospects.
Journal Article
Automating shareable cyber threat intelligence production for closed source software vulnerabilities: a deep learning based detection system
Software can be vulnerable to various types of interference. The production of cyber threat intelligence for closed source software requires significant effort, experience, and many manual steps. The objective of this study is to automate the process of producing cyber threat intelligence, focusing on closed source software vulnerabilities. To achieve our goal, we have developed a system called cti-for-css. Deep learning algorithms were used for detection. To simplify data representation and reduce pre-processing workload, the study proposes the function-as-sentence approach. The MLP, OneDNN, LSTM, and Bi-LSTM algorithms were trained using this approach with the SOSP and NDSS18 binary datasets, and their results were compared. The aforementioned datasets contain buffer error vulnerabilities (CWE-119) and resource management error vulnerabilities (CWE-399). Our results are as successful as the studies in the literature. The system achieved the best performance using Bi-LSTM, with F1 score of 82.4%. Additionally, AUC score of 93.0% was acquired, which is the best in the literature. The study concluded by producing cyber threat intelligence using closed source software. Shareable intelligence was produced in an average of 0.1 s, excluding the detection process. Each record, which was represented using our approach, was classified in under 0.32 s on average.
Journal Article