Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-6-27 10:30'''
|time='''2026-01-30 10:30'''
|addr=4th Research Building A527-B
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
|abstract = LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.
|confname =SenSys'25
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|speaker=Kai Chen
|date=2026-1-30
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Recent advances in network and mobile computing.  
|abstract =Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model's capability to generate precise and relevant content, making the extracted content easy to parse.
|confname=talk
|confname =WWW'25
|link=[Resource:Paper Carnival 2022|Paper Carnival 2022
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|title=]
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|speaker=all
|speaker=Daobin
 
|date=2026-1-30
 
 
}}
}}
'''Visible Light Communication--Wenliang'''
[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485934 CurveLight: An Accurate and Practical Indoor Positioning System]
[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485948 SpiderWeb: Enabling Through-Screen Visible Light Communication]
[Kaiwen][ICNP2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9155474 X-MAC: Achieving High Scalability via Imperfect-Orthogonality Aware Scheduling in LPWAN]
'''Response to Mobility--Luwei'''
[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796811 Enabling QoE Support for Interactive Applications over Mobile Edge with High User Mobility]
[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796968 User Experience Oriented Task Computation for UAV-Assisted MEC System]
[TMC2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9343712 ECHO: Efficient Zero-Control-Packet Broadcasting for Mobile Ad Hoc Networks]
[Zhuoliu][MobiCom21] [https://www.microsoft.com/en-us/research/uploads/prod/2021/09/Visage_Mobicom_2021.pdf Visage: enabling timely analytics for drone imagery]
'''Offloading, Delivery--Wenjie'''
[Infocom2022] [https://ieeexplore.ieee.org/document/9796843 An Efficient Two-Layer Task Offloading Scheme for MEC Networks with Multiple Services Providers]
[Infocom2022] [https://ieeexplore.ieee.org/document/9796714/ Two Time-Scale Joint Service Caching and Task Offloading for UAV-assisted Mobile Edge Computing]
[Infocom2022] [https://ieeexplore.ieee.org/document/9796763/ AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web]
[TMC2022] [https://ieeexplore.ieee.org/document/9238459 A Force-Directed Approach to Seeking Route Recommendation in Ride-on-Demand Service Using Multi-Source Urban Data]
[Xinyu][INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796908/ IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes]
[Jiajun][INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796661/ Kalmia: A Heterogeneous QoS-aware Scheduling Framework for DNN Tasks on Edge Servers]
'''Video Service in Edge Networks--Congrong'''
[SigComm 2022] [https://dl.acm.org/doi/pdf/10.1145/3544216.3544218 NeuroScaler: neural video enhancement at scale]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796984/ FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796657/ DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation]
[MobiHoc 2021] [https://dl.acm.org/doi/pdf/10.1145/3466772.3467034 Task Offloading with Uncertain Processing Cycles]
'''Edge, offloading, caching--Qingyong'''
[Infocom 2022] [https://ieeexplore.ieee.org/document/9796969/ Online File Caching in Latency-Sensitive Systems with Delayed Hits and Bypassing]
[Infocom 2022] [https://dl.acm.org/doi/10.1109/INFOCOM48880.2022.9796799 Distributed Cooperative Caching in Unreliable Edge Environments]
[TMC 2022] [https://ieeexplore.ieee.org/abstract/document/9832640 Reverse Auction-based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing]
[YuanQi][NSDI 2022] [https://www.microsoft.com/en-us/research/uploads/prod/2021/07/nsdi22spring-final74.pdf Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers]
[Wangkun][Infocom 2022][https://ieeexplore.ieee.org/document/9796884/ Joint Resource Management and Flow Scheduling for SFC Deployment in Hybrid Edge-and-Cloud Network]
'''Communication-Efficient Federated Learning--Jianqi'''
[ICML 2022] [https://arxiv.org/pdf/2111.00465.pdf DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning]
[ICML 2022] [https://proceedings.mlr.press/v162/yi22a/yi22a.pdf QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796982/ Optimal Rate Adaption in Federated Learning with Compressed Communications]
'''Crowdsensing-Xianyang'''
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796960/ Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796743/ Worker Selection Towards Data Completion for Online Sparse Crowdsensing]
[IoTJ] [https://ieeexplore.ieee.org/document/9828398/ Nondeterministic Mobility based Incentive Mechanism for Efficient Data Collection in Crowdsensing]
[Jiangshu][SIGCOMM2022] [https://dl.acm.org/doi/pdf/10.1145/3544216.3544238 From Luna to Solar: The Evolutions of the Compute-to-Storage Networks in Alibaba Cloud]
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:51, 30 January 2026

Time: 2026-01-30 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [SenSys'25] MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments, Kai Chen
    Abstract: LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.
  2. [WWW'25] Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition, Daobin
    Abstract: Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model's capability to generate precise and relevant content, making the extracted content easy to parse.

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

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