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5th international conference on Blockchain computing and Applications, BCCA 2023

作者： Charoenwong, Ben Kirby, Robert M. Reiter, Jonathan Business School National University of Singapore Singapore Singapore School of Computing University of Utah Salt Lake City United States Data Finnovation Singapore Singapore

ISBN: (纸本)9798350339239

Decentralized Finance (DeFi) aims to use advancements in both computation and cryptography to tackle economic problems. Therefore, it must operate within the intersection of constraints from both the computer science and economic domains. We explore a foundational question at the junction of those fields: Can we synthesize variable market-clearing risk-free yield for native tokens via smart contracts? We use a stylized model representing a large class of decentralized consensus algorithms to show this is impossible. This undecidability result places bounds on what decentralized financial products can be built and constrains the shape of future developments in DeFi. Among other limitations, our results reveal that markets in DeFi are incomplete. © 2023 IEEE.

关键词： Finance

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New Strategy for Developing and Enhancing Online Analytical processing Cubes on Graphs 14

New Strategy for Developing and Enhancing Online Analytical ...

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14th international conference on Intelligent Systems: Theories and Applications, SITA 2023

作者： Labzioui, Redouane Letrache, Khadija Ramdani, Mohammed University Hassan Ii Lim Laboratory Faculty of Sciences and Techniques of Mohammedia Department of Informatics Casablanca Morocco

ISBN: (纸本)9798350308211

The growth of data had a profound impact on contemporary databases, leading to the emergence of a variety of No Sql (Not Only SQL) databases, containing those designed for graph data. To seamlessly incorporate graph data into existing decision support systems, there is a need to develop new data Warehouse (DW) models that harness the advantages of graphs. This study introduces an innovative approach for creating a DW using a graph database. Furthermore, the research suggests employing aggregation algorithms to enhance the effectiveness of data analysis within a DW built upon a graph-oriented framework, addressing the challenge of building optimized OLAP (Online Analytical processing) models within graph structures and emphasizing the selection of pertinent aggregations to effectively meet the diverse requirements of users. © 2023 IEEE.

关键词： data warehouses

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Research on Demand Response data Compression Based on Neural Network under 5G Environment 5

Research on Demand Response Data Compression Based on Neural...

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5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Pan, Yuqing Qi, Bing Li, Bin Zhu, Enguo Liu, Yan Lu, Chunguang Wang, Chaoliang School of Electrical and Electronic Engineering North China Electric Power University Beijing China China Electric Power Research Institute Beijing China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

ISBN: (纸本)9798350333442

In the context of 5G, data volume is exploding, and data transmission and processing are facing challenges. In this paper, a data compression algorithm based on neural network is proposed to solve the problem of large scale and high dimension of demand response data in the process of transmission and storage. By mapping demand response data to low dimensional space for representation, the algorithm effectively reduces the amount of data and improves the efficiency of data transmission and storage. The algorithm uses Long Short Term Memory network for data compression and restoration. The experimental results show that the data compression algorithm based on neural network has good compression effect and accuracy. At the same time, the algorithm has good versatility and adaptability, and can be applied to the compression and restoration of data sets of different types and sizes, providing an effective solution for efficient demand response data processing. © 2023 IEEE.

关键词： Restoration

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Empowering Scientific Discovery Through computing at the Advanced Photon Source

Empowering Scientific Discovery Through Computing at the Adv...

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2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Parraga, Hannah Hammonds, John Henke, Steven Veseli, Siniša Allcock, William Côté, Benoit Chard, Ryan Narayanan, Suresh Schwarz, Nicholas X-ray Science Division Argonne National Laboratory LemontIL United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9798400707858

This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication. © 2023 ACM.

关键词： Information management

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Research on Mobile Network User Complaint Warning Method Based on Multimodal data 8

Research on Mobile Network User Complaint Warning Method Bas...

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IEEE 8th international conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Di, Zixiang Xiao, Tian Li, Bei Peng, Yunfeng Li, Jianfei Cheng, Xinzhou Xu, Lexi Zhu, Xiaomeng Zheng, Hangbo China United Network Commun Corp Res Inst Beijing 100037 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China Zhejiang Airport Digital Technol Co Ltd Hangzhou 310051 Peoples R China

ISBN: (纸本)9798350319514

With the rapid development of information and communication technology, the types of service applications for mobile terminals are becoming increasingly diverse. Each application has varying requirements for network performance, and each user's preferences, usage frequency, and sensitivity are different. When the quality of the operator network cannot meet the specific service needs of users and exceeds the threshold that users can tolerate, it will trigger user complaints. This article proposes a method for mobile network user complaint warning based on multimodal data. The method can be used to identify potential complaint users in the network and reduce the number of user complaints. In this method, a multimodal user level application rating feature dataset is constructed based on multi-source data, combined with factors such as user preferences and usage perception. Then, AI algorithms and great network computing power are used to generate complaint prediction models for different service applications at the user level. By integrating multiple application business complaint models at the user level and comprehensively evaluating user complaint expectations, it is used to improve the accuracy of user complaint warning.

关键词： Mobile service applications Artificial intelligence algorithms Complaint warning Multi-source data Multimodal

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Intra-day optimal power flow considering flexible workload scheduling of IDCs

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ENERGY REPORTS 2023年 9卷 1149-1159页

作者： Li, Chuyi Zheng, Kedi Guo, Hongye Cai, Yuanji Chen, Qixin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

The online interactive workloads and offline batch computing services of Internet data centers (IDCs) have scheduling flexibility. Batch computing tasks with low latency sensitivity and priority can adjust the processing time and provide flexibility temporally. In contrast, interactive workloads with high latency sensitivity and priority can change the physical server for processing and provide flexibility spatially. Considering the change of power supply cost with time and space, the load flexibility of IDCs brought by Internet workload scheduling can be utilized to respond to the power system for more economical operation. To take full advantage of the flexibility of IDCs' load demand, IDCs coordinated by an Internet service company (ISC) as a special controllable load with both temporal and spatial flexibility potential are considered. Instead of operating separately in normal optimal power flow (OPF) models, a two-stage interactive framework of the distribution system operator (DSO) and ISC is established. The operating state of the ISC is considered in OPF and is introduced as regularization terms into the objective function, thus fully exploiting its load flexibility. Some operation constraints are also added to guarantee the quality of service (QoS) of the ISC. The effectiveness of the model and the interactive framework are evaluated in a typical 33-bus medium-voltage distribution network. The results validate the further reduction of the power grid operation cost compared with the traditional OPF operation. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Optimal power flow Internet data center Load flexibility

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An Efficient Privacy-preserving Join-Aggregation Scheme in Smart Grids 9

An Efficient Privacy-preserving Join-Aggregation Scheme in S...

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9th IEEE international conference on Big data Security on Cloud (BigdataSecurity) / 9th IEEE international conference on High Performance and Smart computing (IEEE HPSC) / 9th IEEE international conference on Intelligent data and Security (IEEE IDS)

作者： Zhou, Peng Qi, Weiqiang Sun, Jiasai Xu, Zichao State Grid Zhejiang Elect Power Corp Informat & T Hangzhou Peoples R China

ISBN: (纸本)9798350312935

data aggregation is a common technique used in the Internet of Things (IoT) to combine fine-grained data from different sources. With increasing demands for data privacy, it has become a hot research topic. How to perform data aggregation without privacy leakage is a challenge. In smart grids, power data often has the characteristics of large volume and high sensitivity, while the limited computing resources of power bureaus can lead to corresponding delays and decreased service quality. This paper proposes an efficient and lightweight privacy-preserving scheme for data aggregation with the background of smart grid data aggregation. The proposed scheme ensures the privacy and security of sensitive data when processing join-aggregation task. The security of the scheme in the semi-honest model is proved using the Real-Ideal paradigm. Finally, the computational and communication costs of the proposed scheme are analyzed and compared, which shows that the total number of homomorphic operations is fewer than that of other related literature. The communication cost of the proposed scheme is four rounds, which is communication friendly.

关键词： Secure multi-party computation Smart grid data aggregation Homomorphic encryption Semi-honest Security

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Construction of an Intelligent Student Management and Evaluation Information Platform Based on data Evaluation algorithms

Construction of an Intelligent Student Management and Evalua...

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2023 international conference on data Science and Network Security, ICDSNS 2023

作者： Liu, Yue Ren, Tiannan College of Food Science and Technology Wuhan Business University Wuhan China No.3 Middle School of Yichang Yichang China

ISBN: (纸本)9798350301595

With the development of higher education, the target demand for student management and evaluation information systems is constantly increasing. Web mining technology based on computer data analysis algorithms can efficiently respond to the increasingly intelligent interactive information management platform. How to improve the quality of system database management is a key issue that urgently needs to be addressed in the entire platform design. This paper examined the use of Web mining technology to build a student information management platform, aiming to improve the processing efficiency of the system and reduce user waiting time. Through the information management platform and its database design and simulation experiment analysis, this paper used the Web mining technology based on data analysis algorithm and combined with the priority ranking of the information management platform function modules. Based on the data results, the following conclusions could be drawn: Against the backdrop of the continuous improvement in the development speed of information management technology, information management systems that used web mining algorithms had higher system processing efficiency in various functional modules than those that did not use web mining technology, with an average improvement of 31%. Meanwhile, systems that used web mining technology had lower user waiting times compared to those that did not use web mining technology, with an average reduction of 2.2 minutes. This indicated that an information management platform based on web mining technology had a higher overall system processing efficiency and lower user waiting time. © 2023 IEEE.

关键词： Information management

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OSMO: Enhanced Offloading for data Stream Perception with Smoothness and Orderliness 29

OSMO: Enhanced Offloading for Data Stream Perception with Sm...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Chaokun Fan, Quan Jinlong, E. He, Shen Tianjin University College of Intelligence and Computing Tianjin China Digital City Technology Co. Ltd. China Xiongan Group Xiong'an China University of California Riverside United States Renmin University of China School of Information Beijing China Engineering Research Center of Database and Business Intelligence Moe China

ISBN: (纸本)9798350330717

On-device AI is taking over our daily lives by moving closer to mobile devices as perception applications. A data stream perception application generally has three essential requirements: timeliness, smoothness, and orderliness. Most researchers' efforts to date have proposed various offloading approaches to accelerate compute-intensive AI algorithms in perception applications, thereby fulfilling the requirement of timeliness. However, the lack of concern about the smoothness and orderliness of the data stream will result in fluctuation and commotion anomalies that greatly impair the user experience. In this paper, we propose an enhanced Offloading System with sMoothness and Orderliness (OSMO) to guarantee perception applications' smooth refresh rates while processing data streams in proper orders with low overhead. OSMO takes advantage of heterogeneous computing devices and data-level parallelism in the offloading process. A scheduling strategy is further devised that dynamically tunes a set of parameters to achieve the best trade-offs among the three requirements of perception applications. We implement a prototype system based on TensorFlow and its typical Android demos. Real-world evaluations demonstrate that our solution can effectively address the fluctuation and commotion issues while providing a high data processing rate with multi-device collaboration. © 2023 IEEE.

关键词： data stream perception Offloading Orderliness Smoothness

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Direct Transposition Interleaving Technique for DNA data Storage 7

Direct Transposition Interleaving Technique for DNA Data Sto...

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7th international conference on Biomedical Engineering and Applications (ICBEA)

作者： Li, Zhengyu Bi, Kun Zhao, Xiangwei Southeast Univ Monash Univ Joint Grad Sch Suzhou Peoples R China Southeast Univ State Key Lab Bioelect Sch Biol Sci & Med Engn Nanjing Peoples R China

ISBN: (纸本)9798350323238

Compared to traditional storage media, DNA possesses several advantages;however, it is prone to errors in its base sequence during the storage process. When employing traditional error correction coding algorithms for DNA storage, a substantial number of redundant bases are added to ensure lossless data recovery. This approach results in significant base consumption and is ill-suited for scenarios with extreme error cases. To address these issues, this paper proposes the Direct Transpose Interleaving Code (DTIC), which uses data interleaving technology to distribute potential errors in a sequence across multiple sequences. DTIC can be used in conjunction with traditional error correction coding algorithms to enhance data recovery capabilities. Through theoretical analysis and simulation experiments, this paper demonstrates that lossless data recovery necessitates fewer redundant bases with DTIC processing compared to data without DTIC processing.

关键词： DNA storage base sequence error correction coding DTIC data recovery capability

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