Program

Times: Monday, November 4, 2024

Venue: Diamond Ballroom, Sub-Ballroom 4

The Dragon Hotel Hangzhou, 120 Shuguang Road, Xihu Area, Hangzhou, China

9:00-9:10 Opening Session

9:10-9:50 Keynote

Building Trustworthy LLM System

Prof. Shuai Wang (Hong Kong University of Science and Technology)

10:00-11:00 Session 1

10:00-10:15

Data Collection, Analysis and Application of Multimodal Human Gait Information

Wenjing Gao (Shandong Industrial Internet Development Research Center), Weiwei Ma (Shandong Industrial Internet Development Research Center), Cheng Zhou (Shandong Industrial Internet Development Research Center), Xingzhao Cao (Shandong Industrial Internet Development Research Center), Liming Hu (Shandong Dujiaoshou Cloud Technology Co. Ltd), Guochang Wang (Shandong Dujiaoshou Cloud Technology Co. Ltd), Aiyun Li (Shandong Dujiaoshou Cloud Technology Co. Ltd)

10:15-10:30

Exploratory Practice of SME Digital Transformation via Industrial Internet: A Case of Self-Developed APP

Wenjing Gao (Shandong Industrial Internet Development Research Center), Xingzhao Cao (Shandong Industrial Internet Development Research Center), Cheng Zhou (Shandong Industrial Internet Development Research Center), Jian Wang (Shandong Industrial Internet Development Research Center), Lianxin Song (Shandong Industrial Internet Development Research Center), Jiawei Li (Shandong Industrial Internet Development Research Center)

10:30-10:45

PCB Defect Detection Based on Evolutionary Object Detection Algorithm

Xiaohui Liu (China Academy of Industrial Internet), Haoxiang Zhang (China Academy of Industrial Internet), Weixi Chang (Beijing Ensonic tech CO., LTD), Zexiao Xiao (China Academy of Industrial Internet), Zhiru Li (China Academy of Industrial Internet), Xiangman Song (Northeastern University), Guowei Zhu (China Academy of Industrial Internet)

10:45-11:00

Time-Series Prediction Algorithm for Boiler Power Generation Steam Temperature

Hexiao Zhou (China Academy of Industrial Internet), Haoxiang Zhang (China Academy of Industrial Internet), Dongliang Ding (Beijing Ensonic tech CO., LTD), Guowei Zhu (China Academy of Industrial Internet)

11:00-11:10 Tea Break

11:10-12:10 Session 2

11:10-11:25

A Benchmark Dataset for Evaluating Spatial Perception in Multimodal Large Models

Xuan Li (Beijing Information Science and Technology University), Haoxiang Zhang (China Academy of Industrial Internet), Baozheng Jiang (China Academy of Industrial Internet), Yanxia Li (China Academy of Industrial Internet), You Li (China Academy of Industrial Internet)

11:25-11:40

A Practical Investigation of the Accuracy of Large Language Models in Various Industrial Application Scenarios

Baozheng Jiang (China Academy of Industrial Internet), Haoxiang Zhang (China Academy of Industrial Internet), Yanxia Li (China Academy of Industrial Internet), Hexiao Zhou (China Academy of Industrial Internet), Zexiao Xiao (China Academy of Industrial Internet), Sijia He (China Academy of Industrial Internet), Wenying Qiu (China Academy of Industrial Internet), You Li (China Academy of Industrial Internet)

11:40-11:55

Cross-modal Retrieval Based on Multi-modal Large Model With Convolutional Attention and Adversarial Training

Haijing Nan (China Telecom Cloud Computing Corporation), Zicong Miao (China Telecom Cloud Computing Corporation), Kehan Wang (China Telecom Cloud Computing Corporation), Weize Li (China Telecom Cloud Computing Corporation), Hui Chen (China Telecom Cloud Computing Corporation), Xiaoqing Wu (China Telecom Cloud Computing Corporation), Xiaodong Pan (China Telecom Cloud Computing Corporation), Wenying Qiu (China Academy of Industrial Internet), Haoxiang Zhang (China Academy of Industrial Internet)

11:55-12:10

Assessing the Potential of Large Language Models for Chemical Engineering Applications

Xuan Li (Beijing Information Science and Technology University), Haoxiang Zhang (China Academy of Industrial Internet), Baozheng Jiang (China Academy of Industrial Internet), You Li (China Academy of Industrial Internet)

12:10-12:15 Closing Remarks

Summary

The topic of IoT (Internet of Things) datasets for multi-modal large models are is centered on the integration and utilization of diverse data sources generated by IoT devices for training and enhancing the capabilities of large-scale machine learning models. IoT datasets typically involve a wide range of data types, including but not limited to sensor data, video streams, audio signals, and text logs. These datasets capture various aspects of the physical world, from environmental conditions to human activities, and provide rich contextual information for model training. The key aspect of multi-modal large models is their ability to process and analyze multiple types of data simultaneously. By leveraging IoT datasets, these models can gain a deeper understanding of the world by combining different types of information. For example, a model trained on both sensor data and video streams can detect patterns and correlations between environmental conditions and human behavior, enabling more accurate predictions and decision-making. There are still many challenges and opportunities associated with handling large-scale, distributed, and heterogeneous IoT data. This includes issues such as data collection, preprocessing, storage, and privacy concerns, as well as the development of efficient algorithms and techniques for data integration and analysis. To overcome these challenges, we would like to invite worldwide researcher to share and present their latest research progresses on datasets, methodology and application about IoT datasets for multi-modal large models, including but not limited to the following main topics.

Main Topics

• New datasets, or collections of datasets based on previously available data
• Data-centric multi-modal large models with new insight
• Advanced practices in data collection and curation
• Benchmarks on new or existing datasets, as well as benchmarking tools
• Frameworks for responsible dataset development and analysis
• In-depth analyses of IoT datasets for multi-modal large models
• Systematic analyses of existing systems on novel IoT datasets yielding important new insight

Submission Guide

Please follow the submission guideline from the ACM SenSys 2024 Submission Website. Please specify that your paper is for the The First International Workshop on IoT Datasets for Multi-modal Large Model. All papers accepted and presented at SenSys 2024 will be included in the workshop proceedings published by ACM Digital Library, which are typically indexed by EI.

Submission Link

Please submit the paper to https://iot-mmlm24.hotcrp.com/

Important Dates

• 25 September, 2024, 23:59 AoE: Workshop Papers Submission Deadline
• 2 October, 2024: Paper Acceptance Notification
• 16 October, 2024, 23:59 AoE: Workshop Paper Camera Ready

Paper Types

We solicit three types of paper submission:
• Technical Papers up to 6 pages (including everything except references), reporting on novel research, to be presented at the workshop as an oral presentation;
• Position Papers up to 4 pages (including everything), reporting on new research directions to be presented at the workshop as an oral presentation with interactive discussion;
• Demo/Poster Papers up to 2 pages (including everything), to be presented at the workshop as an interactive demonstration or poster respectively.

Organizers

Dr. Weixi Gu, China Academy of Industrial Internet, China (guweixigavin@gmail.com)

Weixi Gu received the Ph.D. degree from Tsinghua University (THU), and the Bachelor degree from Shanghai Jiao Tong University. He was the Postdoc at University of California, Berkeley. He is currently a principal researcher at China Academy of Industrial Internet (CAII). His research interests include mobile computing, Industrial Internet of Things, and machine learning.

Dr. Shuai Wang, Hong Kong University of Science and Technology, China (shuaiw@cse.ust.hk)

Dr. Shuai Wang is an Assistant Professor at the Department of Computer Science and Engineering (CSE), the Hong Kong University of Science and Technology (HKUST). He received his Ph.D. from Penn State University, and B.S. from Peking University. Before joining HKUST, He was a post-doctoral researcher at ETH Zurich. He is broadly interested in computer security. Shuai was awarded the Early Career Award by the Research Grants Council (RGC) of the Hong Kong government in 2020, 2023 Alibaba DAMO Academy Promising Scientist Award, and was twice awarded the CCF Rhino-Bird Young Faculty Open Research Awards (2020; 2022). His research on launching and mitigating attacks on AI systems has received the Google Research Scholar Award 2023. Shuai and his students received the ACM SIGSOFT Distinguished Paper Award at ASE'23.

Dr. Weiwei Jiang, Beijing University of Posts and Telecommunications, China (jww@bupt.edu.cn)

Weiwei Jiang received the B.Sc. and Ph.D. degrees from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2013 and 2018, respectively. He is currently an assistant professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His current research interests include artificial intelligence for networking and communication, satellite communication and smart grid communication.

Program Committee Members

• Chunhui Duan, Beijing Institute of Technology
• Junbo Zhang, JD Intelligent Cities Research
• Xi Xiong, Google
• Daoyuan Wu, Nanyang Technological University
• Le Guan, University of Georgia
• Xing Gao, University of Delaware
• Yuqing Gao, Tongji University
• Fei Ma, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)
• Yuan Gao, Shanghai University
• Wendong Mao, Sun Yat-Sen University
• Xin Liu, Lanzhou University
• Jinpeng Chen, Beijing University of Posts and Telecommunications
• Huaming Wu, Tianjin University
• Jinglei Tan, Henan Key Laboratory of Information Security
• Fan Wu, Central South University
• Yuankai Wu, Sichuan University
• Qinghe Zheng, Shandong Management University
• Hongyun Chu, Xi'an University of Posts & Telecommunications
• Chenlu-Zhuansun, Guangzhou University
• Chao Wang, Jiangxi Normal University
• Hanqing Guo, University of Hawaii at Mānoa
• Zihan Chen, Singapore University of Technology and Design
• Xuyu Wang, Florida International University
• Shaohu Zhang, University of North Carolina at Pembroke
• Yi Ding, UT Dallas
• Letian Zhang, Middle Tennessee State University
• Jiechao Gao, University of Virginia
• Linxi Zhang, Central Michigan University
• Fei Dou, University of Georgia
• Yi Liu, City University of Hong Kong
• Jianyi Zhang, Duke University