Po Yang

University of Sheffield

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Jun Qi

Xi’an Jiaotong-Liverpool University

Yun Yang

Yunnan University

Menghui Zhou

Zhejiang Chinese Medical University

Xulong Wang

Shandong University

Ping Li

Hong Kong Polytechnic University

Fengtao Nan

Yunnan University

Bin Sheng

Shanghai Jiao Tong University

Bin Sheng

Shanghai Jiao Tong University

Rujing Wang

Hefei Institutes of Physical Science

Khan Muhammad

Sungkyunkwan University

Cooperative Institutions

Yunnan University

Chinese Academy of Sciences

Shanghai Jiao Tong University

University of Sheffield

Zhejiang University

University of Electronic Science and Technology of China

Liverpool John Moores University

University of Science and Technology of China

Huazhong University of Science and Technology

Fudan University

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Multi-Scale and Multi-Level Feature Assessment Framework for Classification of Parkinson's Disease State From Short-Term Motor Tasks

IEEE Transactions on Biomedical Engineering (2025)

Xiyang Peng Yuting Zhao Ziheng Li Xulong Wang Fengtao Nan

Feature (linguistics)

10.1109/tbme.2024.3418688

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Citations (0)

Robust Temporal Smoothness in Multi-Task Learning

Proceedings of the AAAI Conference on Artificial Intelligence (2023)

Menghui Zhou Yu Zhang Yun Yang Tong Liu Po Yang

Multi-task learning models based on temporal smoothness assumption, in which each time point of a sequence of time points concerns a task of prediction, assume the adjacent tasks are similar to each other. However, the effect of outliers is not taken into account. In this paper, we show that even only one outlier task will destroy the performance of the entire model. To solve this problem, we propose two Robust Temporal Smoothness (RoTS) frameworks. Compared with the existing models based on temporal relation, our methods not only chase the temporal smoothness information but identify outlier tasks, however, without increasing the computational complexity. Detailed theoretical analyses are presented to evaluate the performance of our methods. Experimental results on synthetic and real-life datasets demonstrate the effectiveness of our frameworks. We also discuss several potential specific applications and extensions of our RoTS frameworks.

Smoothness

Sequence (biology)

10.1609/aaai.v37i9.26351

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Citations (8)

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

IEEE Transactions on Industrial Informatics (2018)

Bin Cao Jianwei Zhao Po Yang Zhihan Lv Xin Liu

Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage, lifetime, and reliability. Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.

10.1109/tii.2018.2803758

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Citations (57)

Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

IEEE Transactions on Systems Man and Cybernetics Systems (2018)

Khan Muhammad Jamil Ahmad Zhihan Lv Paolo Bellavista Po Yang

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.

Fire Detection

10.1109/tsmc.2018.2830099

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Citations (411)

Distributed Training for Speech Recognition using Local Knowledge Aggregation and Knowledge Distillation in Heterogeneous Systems

Hongrui Shi Valentin Radu Po Yang

Data privacy and data protection are crucial issues for automatic speech recognition (ASR) system when relying on client generated data for training. The best protection is achieved when training is distributed fashion, close to the client local data, rather than centralising the training. However, distributed training suffers from system heterogeneity, due to clients having unequal computation resources, and data heterogeneity, due to training data being non-independent and identically distributed (non-IID). To tackle these challenges, we introduce FedKAD, a Federated Learning (FL) framework that uses local Knowledge Aggregation over top level feature maps and Knowledge Distillation. We show that our FedKAD achieves better communication efficiency than standard FL methods that use uniform models, due to transferring parameters of smaller size client models, and overall better accuracy than FedMD, an alternative KD-based approach designed for heterogeneous data. Our work enables faster, cheaper and more inclusive participation of clients in heterogeneous distributed training.

Federated Learning

Feature (linguistics)

Training set

10.1145/3578356.3592591

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Citations (2)

Life-Logging Data Aggregation Solution for Interdisciplinary Healthcare Research and Collaboration

Zhikun Deng Po Yang Youbing Zhao Xia Zhao Feng Dong

The wide-spread use of wearable devices and mobile apps in the Internet of Things (IoT) environments makes effectively capture of life-logging personal health data come true. A long-term collection of these health data will benefit to interdisciplinary healthcare research and collaboration. But most wearable devices and mobile apps in the market focus on personal fitness plan and lack of compatibility and extensibility to each other. Existing IoT based platforms rarely achieve a successful heterogeneous life-logging data aggregation. Also, the demand on high security increases difficulties of designing reliable platform for integrating and managing multi-resource life-logging health data. This paper investigates the possibility of collecting and aggregating life-logging data with the use of wearable devices, mobile apps and social media. It compares existing personal health data collection solutions and identifies essential needs of designing a life-logging data aggregator in the IoT environments. An integrated data collection solution with high secure standard is proposed and deployed on a state-of-the-art interdisciplinary healthcare platform: MHA [15] by integrating five life-logging resources: Fitbit, Moves, Facbook, Twitter, etc. The preliminary experiment demonstrates that it successfully record, store and reuse the unified and structured personal health information in a long term, including activities, location, exercise, sleep, food, heat rate and mood.

Data logger

10.1109/cit/iucc/dasc/picom.2015.342

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Citations (33)

A novel sub-Kmeans based on co-training approach by transforming single-view into multi-view

Future Generation Computer Systems (2021)

Fengtao Nan Yahui Tang Po Yang Zhenli He Yun Yang

Co-training

K-Means Clustering

Economic shortage

10.1016/j.future.2021.07.019

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Citations (15)

A weighted multi-feature transfer learning framework for intelligent medical decision making

Applied Soft Computing (2021)

Yun Yang Jing Guo Qiongwei Ye Yuelong Xia Po Yang

Transfer of learning

Feature (linguistics)

Transformative Learning

Ensemble Learning

10.1016/j.asoc.2021.107242

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Citations (15)

A Survey on Quantitative Evaluation of Web Service Security

2015 IEEE Trustcom/BigDataSE/ISPA (2016)

Bo Zhou Qi Shi Po Yang

The number of web services available on the Internet has grown rapidly. Service consumers face a hard decision over which service to choose among the available ones. Security holds a key after various vulnerabilities have been exploited by attackers on number of notable web services. This paper carries out a survey on how security has been expressed and promised for web services, through both the Web Service Description Language and Service Level Agreements. It reviews existing technologies used for comparing individual web services, as well as for service compositions. Taking security into account further complicates the already difficult process of choosing the right service. The paper reveals that despite existing efforts, a quantitative solution needs to be established urgently in order to help service consumers to choose the most secure service for them to use.

WS-Policy

10.1109/trustcom.2016.0130

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Citations (8)

Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition

Computers and Electronics in Agriculture (2020)

Fangyuan Wang Rujing Wang Chengjun Xie Po Yang Liu Liu

10.1016/j.compag.2020.105222

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Citations (98)