Daqing Zhang

EAAI Journal 2026 Journal Article

Skin lesion segmentation method based on lightweight context aware network

• Zhengwei Huang
• Hongmin Deng
• Wentao Tang
• Daqing Zhang

In recent years, significant progress has been made in skin lesion segmentation methods based on deep learning. However, accurately distinguishing the boundaries of lesions from the regions of interest with a small parameter count and low computational complexity still remains challenging. Moreover, real-time performance is also crucial because the rapid acquirement of accurate segmentation results can assist medical professionals in making timely and correct decisions. This paper proposes a lightweight context-aware skin lesion segmentation network (LCS-Net) that features extremely low network complexity and short inference time. The proposed model integrates novel modules for efficient feature extraction, multi-scale context aggregation, and boundary refinement. Finally, we compare LCS-Net with several state-of-the-art methods on three publicly available datasets: skin lesion segmentation datasets provided by international skin imaging collaboration ISIC2017, ISIC2018 and a lung segmentation dataset. Experimental results demonstrate that LCS-Net achieves a Dice coefficient of 89. 41% and a Jaccard Index of 80. 86% on the ISIC2018 dataset, outperforming state-of-the-art methods such as U-Net (Dice: 87. 55%, JI: 77. 86%) and TransFuse (Dice: 89. 27%, JI: 80. 63%). With the parameter count of 0. 07 million and the inference time of 6. 8 ms, LCS-Net consistently outperforms other state-of-the-art networks in segmentation accuracy, computational efficiency, and model size, showing its application potential in resource-constrained.

TIST Journal 2021 Journal Article

MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer

• Yan Liu
• Bin Guo
• Daqing Zhang
• Djamal Zeghlache
• Jingmin Chen
• Sizhe Zhang
• Dan Zhou
• Xinlei Shi

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.

AIJ Journal 2019 Journal Article

Ridesharing car detection by transfer learning

• Leye Wang
• Xu Geng
• Xiaojuan Ma
• Daqing Zhang
• Qiang Yang

Ridesharing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesharing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesharing cars from a pool of cars based on their trajectories. Since licensed ridesharing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i. e. , taxis and buses, to ridesharing detection among ordinary vehicles. We propose a novel two-stage transfer learning framework, called CoTrans. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesharing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesharing detection, and iteratively refine the RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of the RF and CNN to identify the ridesharing cars in the candidate pool. Experiments on real car, taxi and bus traces show that CoTrans, with no need of a pre-labeled ridesharing dataset, can outperform state-of-the-art transfer learning methods with an accuracy comparable to human labeling.

TIST Journal 2017 Journal Article

SPACE-TA

• Leye Wang
• Daqing Zhang
• Dingqi Yang
• Animesh Pathak
• Chao Chen
• Xiao Han
• Haoyi Xiong
• Yasha Wang

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.

TIST Journal 2016 Journal Article

Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks

• Dingqi Yang
• Daqing Zhang
• Bingqing Qu

Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive “home” location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.

TIST Journal 2016 Journal Article

Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features

• Tianben Wang
• Zhu Wang
• Daqing Zhang
• Tao Gu
• Hongbo Ni
• Jiangbo Jia
• Xingshe Zhou
• Jing Lv

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.

TIST Journal 2015 Journal Article

An Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence

• Bin Guo
• Alvin Chin
• Zhiwen Yu
• Runhe Huang
• Daqing Zhang

TIST Journal 2015 Journal Article

EEMC

• Haoyi Xiong
• Daqing Zhang
• Leye Wang
• J. Paul Gibson
• Jie Zhu

Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy—among other things—may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost “for free”: with only an insignificant additional amount of energy required to piggy-back the data—usually incoming task assignments and outgoing sensor results—on top of the call. Here, we present an <i>Energy-Efficient Mobile Crowdsensing</i> (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed <i>EEMC</i> framework outperforms the baseline approaches, and it can reduce overall energy consumption in data transfer by 54--66% when compared to the 3G-based solution.

TIST Journal 2013 Journal Article

Introduction to the special section on intelligent systems for socially aware computing

• Zhiwen Yu
• Daqing Zhang
• Nathan Eagle
• Diane Cook

TIST Journal 2011 Journal Article

Introduction to the special issue on intelligent systems for activity recognition

• Daqing Zhang
• Matthai Philipose
• Qiang Yang