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]
'''Lora--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]
'''Mobility--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]
=== 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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