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Fairness of Large Music Models: From a Culturally Diverse Perspective 9

Fairness of Large Music Models: From a Culturally Diverse Pe...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Qinyuan Gu, Bruce Zhang, He Li, Yunfeng University of Sydney School of Music NSW2008 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China CNPIEC KEXIN Ltd. Beijing100020 China

ISBN: (纸本)9798350391367

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Malicious Client Detection with Differential Privacy Constraints Based on Model Update Slicing and K-Means Clustering

Malicious Client Detection with Differential Privacy Constra...

引用

Frontiers Technology of information and Computer (ICFTIC), IEEE International Conference on

作者： Mengshi Qi Xin Wang Yunpeng He Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Federated learning (FL) is a decentralized machine learning technique that protects data privacy by trainng models locally on devices and sharing only model updates instead of raw data. However, FL also faces multiple security challenges. Clientside poisoning attacks can compromise global model training by injecting malicious updates. Additionally, the server may infer private information from client model updates. Most existing studies address these two risks separately, lacking a systematic analysis and response to their combined threats. This paper proposes a method called Sliced-Clustering Guard (SCGuard), which slices gradients and performs clustering analysis on each segment, enabling the server to more accurately detect malicious updates and effectively differentiate malicious client updates with differential privacy (DP) protection. Experimental results demonstrate that SCGuard can mitigate poisoning attacks while effectively safeguarding clients' sensitive information.

关键词： Training Privacy Differential privacy Systematics Federated learning Scalability Data models Servers Protection Faces

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Univariate Forecasting of time series data Based on Seq2Seq Model 4

Univariate Forecasting of time series data Based on Seq2Seq ...

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4th International Conference on Electronic information Technology and Computer Engineering, EITCE 2020

作者： Guo, Yanwen Wu, Tao Liu, Yanbing Jiang, Yixin Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Internet and Information Sccurity Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781450387811

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction. © 2020 ACM.

关键词： Time series

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Multi-Feature Fusion Identity Generation Method for Fixed Location 5G Terminal Devices 5

Multi-Feature Fusion Identity Generation Method for Fixed Lo...

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5th International Conference on information Technologies and Electrical Engineering, ICITEE 2022

作者： Shao, Zhipeng Wang, Chen Guo, Yaqiong Xu, Junbo Liu, Ruolin State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Jiangsu Nanjing China State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Zhejiang Hangzhou China

ISBN: (纸本)9781450396806

While 5G is developing rapidly, it also faces greater security threats, and achieving reliable terminal identification is a prerequisite for ensuring 5G network security. For the identity reliability problems of the accessed fixed location 5G terminal devices, a multi-feature fusion identity generation method is proposed. Firstly, the channel feature sequence that can reflect the location information of 5G terminal devices is obtained at the base station side based on Sounding reference signal;then the envelope information of the intercepted leading signal is extracted at the base station side using the method of envelope detection in communication reconnaissance to obtain the estimated time width of the leading signal;finally, a multi-feature fusion identity generation method is proposed by combining the permanent identifier SUbscription Permanent Identifier that represents the terminal identity. The study shows that the key authentication based on the fusion of multiple features improves the identification security significantly compared with the key authentication based on the single feature identity, and is also more reliable than other terminal authentication techniques. © 2022 ACM.

关键词： Location

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Time Series Anomaly Detection Based on Graph Convolutional networks 2

Time Series Anomaly Detection Based on Graph Convolutional N...

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2nd International Conference on Applied Machine Learning, ICAML 2020

作者： Hu, Zhiwei Wu, Tao Zhang, Yunan Li, Jintao Jiang, Longsheng Sch. of Commun. and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute China Southern Power Grid Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781728192628

With the popularization of my country's electric vehicles and the continuous deepening of smart grid upgrades, the electricity consumption data of domestic power grid companies has increased significantly. Due to inadequate detection and clever power theft methods of users, a large number of abnormal power data appear in the power consumption data of power companies. Aiming at the power theft problem of power grid companies and abnormal power data, this article suggests a power data outlier detection model in view of the combination of graph convolution and characteristic pool. First, use the time a train of directed horizontal view algorithm to delegate the power data as graph data;then, use the graph convolutional neural network and feature pooling layer to detect the abnormality of the transformed graph data. The validity of the advanced model is proving by standard graph data set and power used record data set of the power grid company. The method proposed in this paper provides an effective solution for the power theft problem and abnormal power data in the power grid. © 2020 IEEE.

关键词： Anomaly detection

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A personalized privacy protection data uploading scheme for mobile crowdsensing 2

A personalized privacy protection data uploading scheme for ...

引用

2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Ma, Rong Chen, Lei Xiong, Jinbo Tian, Youliang Li, Qi Liu, Ximeng Fujian Provincial Key Laboratory of Network Security and Cryptology College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China College of Engineering and Computing Georgia Southern University GA30458 United States Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Fujian Provincial Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781728129778

Mobile crowdsensing is a typical application of the Industrial Internet of hings and brings a promising opportunity to construct powerful industrial systems and services. The existing privacy protection schemes usually adopt a unified privacy protection policy for all sensing data due to lack of effective privacy metrics, thereby resulting in excessive or insufficient protection of privacy information and low accuracy of sensing data. To tackle these issues, in this paper, we propose a personalized privacy protection data uploading (PPDU) scheme based on game theory and sensing user's historical spatio-temporal data. In PPDU, a personalized privacy measurement algorithm is designed to obtain the personalized privacy degree of user sensing data. Based on the privacy degree, a user uploading behavior game is designed by employing an incomplete information game, and a game-based spatio-temporal sensing data uploading method is proposed for user privacy protection. Simulation experiments with real trajectory data show that the PPDU scheme makes a reasonable balance between retaining high quality of crowdsensing service and protecting user's privacy. © 2019 IEEE.

关键词： Game theory

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A Brief Survey on Privacy-Preserving Methods for Graph-Structured Data

A Brief Survey on Privacy-Preserving Methods for Graph-Struc...

引用

International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Zhang, Yunan Wu, Tao Xian, Xingping Xu, Yuqing Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9789811669620

As one of the main manifestations of big data, graph-structured data widely exists in various fields such as social networks, smart cities, medical health, and finance, and is characterized by high dimension, nonlinear, scale-free, small world, etc. Extensive graph-structured data provides sufficient data resources for scientific research and commercial applications. However, graph data mining not only reveals the intrinsic value of data, but also brings the risk of privacy disclosure. Therefore, how to protect graph-structured data privacy is of great significance. In this paper, we survey the very recent research development on privacy preserving methods for graph-structured data. We introduce the common sensitive information and the related privacy risks in graph data, and elaborate the background knowledge for privacy inference attack. Then, the privacy inference attack methods and privacy preservation methods for graph-structured data are summarized. Finally, the shortcomings of the current research about graph-structured data privacy preservation and the possible research directions are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Design and development of smart terminal self-sensing module 5

Design and development of smart terminal self-sensing module

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Qigui, Yao Xiangqun, Wang Xiaojian, Zhang Zhuo, Lv Mingyan, Li Zheng, Zhang Interconnection Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Global Energy Nanjing branch Jiangsu Province China State Grid Henan Electric Power Research Institute State Grid Henan Electric Power Company Henan Province China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

In recent years, with the rapid development of the Internet of Things, the scalability and efficiency of the Internet are not enough to process the big data of the Internet of Things. The edge agent platform tilts the powerful computing and storage capabilities and user-satisfactory business service capabilities to the user side and migrates to the edge of the network to meet the needs of users to securely obtain Internet of Things services. This paper designs a security monitoring module, including host system security monitoring;Docker system security configuration monitoring;container security monitoring;linkage control configuration management and eBPF kernel-level security monitoring. The architecture, technology and design of each part are described in detail, and the interface of the module is configured. It provides a new and available solution for the security protection of edge IoT agents. © 2022 IEEE.

关键词： Internet of things

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Research on data center topological structure analysis technology based on graph database 3rd

Research on data center topological structure analysis techn...

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3rd International Conference on Intelligent, Interactive Systems and Applications, IISA2018

作者： Zhu, Liang Yang, Mingjie Wang, He Global Energy Interconnection Research Institute Co. Ltd. NanjingJiangsu China State Grid Key Laboratory of Information and Network Security Jiangsu China State Grid Gansu Electric Power Co. Ltd. Gansu China

ISBN: (纸本)9783030028039

The data center is the main object of operation and maintenance management. The accurate grasp of all the life cycle of all hardware and software resources and the accurate analysis of the relationship between the operation and maintenance data are the cornerstones of the data center IT architecture operation and maintenance, and also the key to the continuity of the data center to guarantee business continuity. The multi-layer topology self discovery mechanism for business characteristics is established according to the library. Through identification, control and maintenance, it can accurately manage the IT resources of power grid enterprises, realize the changing IT infrastructure of efficient control and management, meet the IT service, and respond quickly, efficiently, accurately and flexibly to the needs of the development of the power Grid Corp business. © Springer Nature Switzerland AG 2019.

关键词： Electric power transmission networks

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