CC* Integration-Large: (BLUE) Software-Defined CyberInfrastructure to enable data-driven smart campus applications

CC* Integration-Large：（蓝色）软件定义的网络基础设施，支持数据驱动的智能校园应用

• 批准号: 2126291
• 负责人: Ming Zhao
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126291&HistoricalAwards=false
• 关键词: CC; Integration; Large; BLUE; Software

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).A new campus infrastructure called BLUE is developed to enable efficient and secure data-driven research and application development based on distributed IoT devices. BLUE addresses three main challenges for supporting innovative smart campus applications based on distributed IoT devices: (a) establishing a programable campus infrastructure to support distributed and ad hoc IoT services, (b) providing strong security and privacy protection of IoT data, and (c) constructing an edge-cloud infrastructure to provide computing, networking, and storage resources to support smart-campus applications. BLUE is a new software-defined infrastructure to support IoT-based data processing, analysis, and distribution over distributed IoT data sources. BLUE also supports a set of tangible metrics, such as network QoS metrics, location, resource consumption, etc., to effectively enable researchers to validate their research models. Moreover, BLUE takes privacy and security protection as a fundamental enabling technique by pushing the computation towards the edge computing and networking infrastructure. Research applications built on this project share a common requirement for low-latency transfer of ever-larger data sets with collaborators across multiple geographic sites. This project will contribute to a national paradigm of campus-level dynamic network services that enables leading-edge network and domain-specific research.BLUE can benefit the full range of campus scholarly activities, including research activities funded by NSF and other federal agencies. The outcomes of this project will be shared with the public based on an open-source license agreement. In addition, undergraduate and graduate student researchers will receive diverse STEM skills training, including networking research, big data analysis, and domain-specific research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。开发了一个名为BLUE的新校园基础设施，以实现基于分布式物联网设备的高效、安全的数据驱动研究和应用开发。BLUE解决了支持基于分布式物联网设备的创新智慧校园应用的三个主要挑战：(a)建立可编程的校园基础设施，以支持分布式和临时物联网服务；(b)为物联网数据提供强大的安全和隐私保护；(c)构建边缘云基础设施，提供计算、网络和存储资源，以支持智慧校园应用。BLUE是一种新的软件定义基础设施，用于支持基于物联网的数据处理、分析和分布式物联网数据源的分发。BLUE还支持一组有形指标，如网络QoS指标、位置、资源消耗等，以有效地使研究人员能够验证他们的研究模型。此外，BLUE将隐私和安全保护作为一种基本的使能技术，将计算推向边缘计算和网络基础设施。基于此项目构建的研究应用程序都有一个共同的需求，即与跨多个地理站点的协作者进行更大的数据集的低延迟传输。该项目将为校园级动态网络服务的国家范例做出贡献，使前沿网络和特定领域的研究成为可能。BLUE可以使所有的校园学术活动受益，包括由NSF和其他联邦机构资助的研究活动。该项目的成果将基于开源许可协议与公众共享。此外，本科生和研究生研究人员将接受多种STEM技能培训，包括网络研究、大数据分析和特定领域研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning

• DOI: 10.1109/icassp49357.2023.10095744
• 发表时间: 2023-03
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Kaiqi Zhao;Yitao Chen;Ming Zhao

Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices

使用可穿戴设备探索基于边缘机器学习的压力预测

• 发表时间: 2022
• 期刊: 21st IEEE International Conference on Machine Learning and Applications
• 作者: S. Sim, T. Paranjpe

Performance Evaluation on CXL-enabled Hybrid Memory Pool

支持 CXL 的混合内存池的性能评估

• 发表时间: 2022
• 期刊: and Storage
• 作者: Q. Yang, R. Jin

Knowledge Distillation via Module Replacing for Automatic Speech Recognition with Recurrent Neural Network Transducer

• DOI: 10.21437/interspeech.2022-500
• 发表时间: 2022-09
• 作者: Kaiqi Zhao;H. Nguyen;Animesh Jain;Nathan Susanj;A. Mouchtaris;Lokesh;Gupta;Ming Zhao

Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning

• DOI: 10.48550/arxiv.2309.06987
• 发表时间: 2023-09
• 期刊: ArXiv
• 作者: Riti Paul;Sahil Vora;Baoxin Li