Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-09-17 8:40
|time='''2026-01-30 10:30'''
|addr=Main Building B1-612
|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
{{Latest_seminar
|abstract=Drowsy driving is one of the biggest threats to driving safety, which has drawn much public attention in recent years. Thus, a simple but robust system that can remind drivers of drowsiness levels with off-the-shelf devices (e.g., smartphones) is very necessary. With this motivation, we explore the feasibility of using acoustic sensors on smartphones to detect drowsy driving. Through analyzing real driving data to study characteristics of drowsy driving, we find some unique patterns of Doppler shift caused by three typical drowsy behaviours (i.e., nodding, yawning and operating steering wheel), among which operating steering wheels is also related to drowsiness levels. Then, a real-time Drowsy Driving Detection system named D^3 -Guard is proposed based on the acoustic sensing abilities of smartphones. We adopt several effective feature extraction methods, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Besides, measures to distinguish drowsiness levels are also introduced in the system by analyzing the data of operating steering wheel. Through extensive experiments with five drivers in real driving environments, D 3 -Guard detects drowsy driving actions with an average accuracy of 93.31%, as well as classifies drowsiness levels with an average accuracy of 86%.
|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=TMC2021
|confname =SenSys'25
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9055089
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|title=Real-Time Detection for Drowsy Driving via Acoustic Sensing on Smartphones
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|speaker=Shiqi Hu
|speaker=Kai Chen
|date=2026-1-30
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=The emerging mobile-edge computing paradigm provides opportunities for the resource-hungry mobile devices (MDs) to migrate computation. In order to satisfy the requirements of MDs in terms of latency and energy consumption, recent researches proposed diverse computation offloading schemes. However, they either fail to consider the potential computing resources at the edge, or ignore the selfish behavior of users and the dynamic resource adaptability. To this end, we study the computation offloading problem and take into consideration the dynamic available resource of idle devices and the selfish behavior of users. Furthermore, we propose a game theoretic offloading method by regarding the computation offloading process as a resource contention game, which minimizes the individual task execution cost and the system overhead. Utilizing the potential game, we prove the existence of Nash equilibrium (NE), and give a lightweight algorithm to help the game reach a NE, wherein each user can find an optimal offloading strategy based on three contention principles. Additionally, we conduct analysis of computational complexity and the Price of Anarchy (PoA), and deploy three baseline methods to compare with our proposed scheme. Numerical results illustrate that our scheme can provide high-quality services to users, and also demonstrate the effectiveness, scalability and dynamic resource adaptability of our proposed algorithm in a multiuser network.
|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=IoTJ2021
|confname =WWW'25
|link=https://ieeexplore.ieee.org/abstract/document/9386238
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|title=D2D-Enabled Mobile-Edge Computation Offloading for Multiuser IoT Network
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|speaker=Wenjie Huang
|speaker=Daobin
|date=2026-1-30
}}
}}
=== 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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