学术活动情况：

1. 博士生/硕士生发表CCF A类/TOP B类顶会论文，实验室将出资支持学生海外参会全部费用。

2. 实验室每年派出学生参加图灵大会学术活动，听取全球计算机领域的院士、技术大牛做学术报告，并现场交流。

3. 近年来，实验室学生海内外参加学术会议情况：

• 2024年10月，澳大利亚，博士生魏昱恒赴澳大利亚墨尔本参加ACM普适与泛在计算国际联合会议UbiComp2024。UbiComp(The ACM international joint conference on Pervasive and Ubiquitous Computing)是普适计算与人机交互领域的国际顶级会议，也是CCF A类推荐会议。魏昱恒在参会期间就被录用论文《AdaStreamLite:Environment-adaptive Streaming Speech Recognition on Mobile Devices》进行口头报告，与领域知名专家学者进行了深入交流。
• 2024年9月，太原，研一硕士研究生生相辉参加第十八届中国物联网学术会议(CWSN 2024)并做论文报告《Dynamic task scheduling strategy for cloud-edge collaborative environment》，论文荣获第十八届中国物联网学术会议(CWSN 2024)优秀论文奖（大会共评选出6篇论文）。
• 2024年8月，天津，硕士研究生董锡耀参加第二十届智能计算国际会议（ICIC 2024)（International Conference on Intelligent Computing 2024），宣讲在线时间序列异常检测论文《Online Multivariate Time Series Anomaly Detection Method Based on Contrastive Learning》，ICIC 2024是CCF推荐C类会议。
• 2024年8月，日本，博士生刘巧巧赴日本岐阜参加第29届数据库系统高级应用国际会议（The 29th International Conference on Database Systems for Advanced Applications,简称DASFAA）。DASFAA是数据库领域的国际权威会议和CCF推荐B类会议。刘巧巧在参会期间就被录用的论文《TDMixer: Lightweight Long-Term Series Forecasting using Time-Continuous Embedding》进行了大会报告，并与参会的学者进行了深入的交流和讨论。
• 2024年8月，瑞典，博士生李华东赴瑞典参加ICPP 2024学术会议并做论文宣讲。ICPP(International Conference on Parallel Processing)国际并行处理会议是计算机学科历史最悠久的国际学术会议之一，今年是该会议的第53届，在8月12日 – 8月15日于瑞典哥特兰岛举办。ACM ICPP是计算机体系结构、并行与分布计算、存储系统领域的顶级会议之一。ACM ICPP收录论文均经过严格的同行双盲评审，近五年平均接收率为27.9%。李博士大会宣讲了两篇论文《RIA: Return on Investment Auto-scaler for Serverless Edge Functions》，《Thawbringer: An Orchestrator to Mitigate Cascading Cold Starts of Serverless Function Chains》进行了大会报告，反响热烈。
• 2018年6月，德国，博士生刘思聪赴德国慕尼黑参加第16届移动系统、应用和服务国际会议（The 16th Annual International Conference on Mobile Systems, Applications, and Services,简称Mobisys），MobiSys偏系统和应用(特别是手机等移动计算系统)，也是CCF B类推荐会议。刘思聪在参会期间就被录用的论文《On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework》进行了大会报告，并与参会的学者进行了深入的交流和讨论。
• 2017年9月，美国，博士生刘思聪赴美国夏威夷参加ACM普适与泛在计算国际联合会议UbiComp2017。UbiComp(The ACM international joint conference on Pervasive and Ubiquitous Computing)是普适计算与人机交互领域的国际顶级会议，也是CCF A类推荐会议。刘思聪在参会期间就被录用的论文《UbiEar: Bringing Location-independent Sound Awareness to the Hard-of-hearing People with Smartphones》进行了大会报告，并与参会的学者进行了深入的交流和讨论。成果获得ACM UbiComp “Distinguished Paper Award”。
• 2016年6月，新加坡，博士生刘思聪赴新加坡参加第14届移动系统、应用和服务国际会议（The 16th Annual International Conference on Mobile Systems, Applications, and Services,简称Mobisys），MobiSys偏系统和应用(特别是手机等移动计算系统)，也是CCF B类推荐会议。刘思聪在参会期间就被录用的论文《Mobiear: Building an Environment-independent Acoustic Sensing Platform for the Deaf using Deep Learning》进行了大会报告，并与参会的学者进行了深入的交流和讨论。
• 博士生刘思聪在老师推荐下，2017年09月-2018年09月，赴美国 Rice University，Department of Electrical and Computer Engineering，联合培养一年，联合培养导师为 Lin Zhong 教授。

部分论文列举如下：

[1] Task Scheduling With UAV-Assisted Dispersed Computing for Disaster Scenario, IEEE Systems Journal, 2022  (2区) 

[2] Question and answer robot's technology for air traffic control[J]. Command Information System and Technology,2021

[3] A Performance Evaluation Method of Load Balancing Capability in SaaS Layer of Cloud Platform , Journal of Physics Conference Series，2021 

[4] Kuberntes集群中多节点合作博弈负载均衡策略 西安电子科技大学学报，Vol.48 page 16-22 and 122,No.6,Dec. 2021 

[5] AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles, "IEEE Transactions on Mobile Computing,Vol.20,Issue 12, page 3282-3297, DECEMBER 2021 (1区)

[6] SmartMeeting: An Novel Mobile Voice Meeting Minutes Generation and Analysis System, Mobile Networks & Applications,vol.25,no.2,pp.521-536,APR 2020  

[7] Privacy Adversarial Network: Representation Learning for Mobile Data Privacy Proceedings of the ACM Interact Mobile Wearable Ubiquitous Technol, Vol.3,No.4, December 2019 (UbiComp 2019 CCF A类会议)

[8] Deep Model Compression for Mobile Platforms: A Survey, TSINGHUA SCIENCE AND TECHNOLOGY, 2019  

[9] On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Seletion Framework, proceedings of the 16th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2018 移动计算旗舰会议)

[10] Understanding Sensor Data using Deep Learning Methods on Resource-Constrained edge devices,""" WIRELESS SENSOR NETWORKS (CWSN 2017),vol.812,pp.139-152,2018 [11th China Wireless Sensor Network Conference (CWSN2017)

[11] ProMETheus: An Intelligent Mobile Voice Meeting Minutes System, Proceedings of the 15th EAI international conference on mobile and ubiquitous systems: computing, networking and services (MobiQuitous 2018),pp.392-401,Nov 2018,New York City, United States.    

[12] UbiEar: Bringing Location-independent Sound Awareness to the Hard-of-hearing People with Smartphones "Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies archive， 2017 

[13] Energy-efficient Algorithm to Construct the Information Potential Field in WSNs,""" IEEE sensor journal,  2017

[14] Poster: MobiEar-building an environment-independent acoustic sensing platform for the deaf using deep learning MobiSys 2016 Companion - Companion Publication of the 14th Annual International Conference on Mobile Systems, Applications, and Services，p 50, June 25, 2016 - June 30, 2016，Singapore

[15] CrowdBlueNet: Maximizing Crowd Data Collection Using Bluetooth Ad Hoc Networks WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS,vol.9798,pp.356-366, WASA 2016.[Proceedings of the 11th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2016,AUG 2016, Bozeman, MT, United states

[16] Air Pollution Source Estimation Profiling via Mobile Sensor Networks "Proceedings of the 2016 International Conference on Computer, Information, and Telecommunication Systems, IEEE CITS 2016,July 2016,KunMing,China

[17] PiPDR:A Placement-Independent Pedestrian Dead Reckoning System in Mobile Phone,""" Ad Hoc & Sensor Wireless Networks,vol.26,no.1-4,pp.73-96,2015

[18] SmartCare: Energy-efficient Long-Term Physical Activity Tracking using Smartphones TSINGHUA Science and Technology, Volume 20,Number 4, pp348-363, August 2015.