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Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction

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arXiv 2023年

作者： Zhu, Meiyi Zecchin, Matteo Park, Sangwoo Guo, Caili Feng, Chunyan Simeone, Osvaldo The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China lab Department of Engineering King's College London LondonWC2R 2LS United Kingdom

In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical information about their local data to the server over a common wireless channel, aiming to enhance the quality of the inference decision at the server. Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision. With federated CP, devices communicate to the server information about the loss accrued by the shared pre-trained model on the local data, and the server leverages this information to calibrate a decision interval, or set, so that it is guaranteed to contain the correct answer with a pre-defined target reliability level. Previous work assumed noise-free communication, whereby devices can communicate a single real number to the server. In this paper, we study for the first time federated CP in a wireless setting. We introduce a novel protocol, termed wireless federated conformal prediction (WFCP), which builds on type-based multiple access (TBMA) and on a novel quantile correction strategy. WFCP is proved to provide formal reliability guarantees in terms of coverage of the predicted set produced by the server. Using numerical results, we demonstrate the significant advantages of WFCP against digital implementations of existing federated CP schemes, especially in regimes with limited communication resources and/or large number of devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Early Warning of City-Scale Unusual Social Event on Public Transportation Smartcard Data

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Early Warning of City-Scale Unusual Social Event on Public T...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Haiyang Wang Xiaming Chen Siwei Qiang Honglun Zhang Yongkun Wang Jianyong Shi Yaohui Jin State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University P. R. China Network and Information Center Shanghai Jiao Tong University P. R. China School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University P. R. China

A sudden social crowd event is serious to public safety as it usually triggers huge number of people who are overwhelming to existing public facilities. Detection, early warning of such social crowd events is very important to the city administration but a very challenging problem in research. In this paper, we aim to solve this problem by using the non-sensitive data from public transportation smartcard. We make a detailed analysis of the traffic of people from smartcard data,, we find a 'two-peak' pattern of human flow before, after a social crowd event happening. Motivated by this finding, we propose a framework for early detection of unusual social crowd events by exploiting time series analysis, machine learning technology. We evaluate our model on the real world public transportation data of the biggest metropolitan of China, validate our model with social crowd event data retrieved from the Internet. The evaluation result shows the effectiveness of our model.

关键词： Public transportation Data models History Mobile handsets Urban areas Hidden Markov models

End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

A NO-REFERENCE QUALITY ASSESSMENT METHOD FOR DIGITAL HUMAN HEAD

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arXiv 2023年

作者： Zhou, Yingjie Zhang, Zicheng Sun, Wei Min, Xiongkuo Ma, Xianghe Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China School of Information and Control Engineering China University of Mining Technology China Peng Cheng Laboratory China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

In recent years, digital humans have been widely applied in augmented/virtual reality (A/VR), where viewers are allowed to freely observe and interact with the volumetric content. However, the digital humans may be degraded with various distortions during the procedure of generation and transmission. Moreover, little effort has been put into the perceptual quality assessment of digital humans. Therefore, it is urgent to carry out objective quality assessment methods to tackle the challenge of digital human quality assessment (DHQA). In this paper, we develop a novel no-reference (NR) method based on Transformer to deal with DHQA in a multi-task manner. Specifically, the front 2D projections of the digital humans are rendered as inputs and the vision transformer (ViT) is employed for the feature extraction. Then we design a multi-task module to jointly classify the distortion types and predict the perceptual quality levels of digital humans. The experimental results show that the proposed method well correlates with the subjective ratings and outperforms the state-of-the-art quality assessment methods. © 2023, CC BY.

关键词： Textures

A No-Reference Quality Assessment Method for Digital Human Head

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A No-Reference Quality Assessment Method for Digital Human H...

IEEE International Conference on Image Processing

作者： Yingjie Zhou Zicheng Zhang Wei Sun Xiongkuo Min Xianghe Ma Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China School of Information and Control Engineering China University of Mining Technology China Peng Cheng Laboratory China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

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Discovering Mass Activities Using Anomalies in Individual Mobility Motifs

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Discovering Mass Activities Using Anomalies in Individual Mo...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhenhuan Li Haiyang Wang Xiaming Chen Yongkun Wang Yaohui Jin Wenjun Zhang Inst. of Image Commun. & Inf. Process. Shanghai Jiao Tong University P. R. China State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University P. R. China Network and Information Center Shanghai Jiao Tong University P. R. China

Mobility motif is a typical topology structure shared by movement trajectories, which could exhibit multi-scale spatio-temporal patterns in human behavior. There are two types of motifs, frequent, infrequent. The infrequent one hasn't got enough attention in research. In this paper, we study the relationship between infrequent motifs, mass activities. We have discovered that crowds are more likely to generate abnormal motifs when they attend activities than on ordinary days. Inspired by the discovery, we propose a novel model to incorporate the connection between motif anomalies, mass activities, which is verified by our real-world data driven analysis. Compared to previous mobility analysis, our work facilitates the inspection of human mobility anomalies in a fresh perspective, it is a promising step towards unraveling the actual causation behind human mobility.

关键词： Buildings Trajectory IEEE 802.11 Standard Data mining Global Positioning system Internet Topology

Hybrid brain/muscle-actuated control of an intelligent wheelchair

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Hybrid brain/muscle-actuated control of an intelligent wheel...

IEEE International Conference on Robotics and Biomimetics

作者： Zhijun Li Shuangshuang Lei Chun-Yi Su Guanglin Li Key Lab of Autonomous System and Network Control Ministry of Education and College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Mechanical and Industrial Engineering Concordia University Montreal Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China

Brain-computer interface (BCI) controlled wheelchair robots can serve as powerful aids for severely disabled people in their daily life, especially to help them move voluntarily. In order to better understand human “thought”, owing to the development of the hybrid brain/muscle interface technique, in this paper, we present a real-time hybrid brain/muscle interface to control a wheelchair directly to keep the disables recovering several motion capabilities by using noninvasive motor imagery Electroencephalography (EEG) and Electromyography (EMG). The EMG and EEG signals from the users are extracted to control the motion of an intelligent wheelchair. Both signals processing consists of off-line training, online control evaluation, and real-time control. An algorithm called the common spatial patterns (CSP) is used in this human-robot system to extract the most discriminative spatial patterns pairs as features. The extensive experiments were conducted on the developed human-wheelchair systems to verify the proposed approaches.

关键词： Wheelchairs Electroencephalography Electromyography Training Robots Muscles Real-time systems

Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit networks

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

A simulator for swarm AUVs acoustic communication networking 11

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A simulator for swarm AUVs acoustic communication networking

11th ACM International Conference on Underwater networks and systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators