Detection and Analysis of Interrupted Behaviors by Public Policy Interventions during COVID-19

Dong, Guimin; Cai, Lihua; Kumar, Shashwat; Datta, Debajyoti; Barnes, Laura E.; Boukhechba, Mehdi

doi:10.1109/chase52844.2021.00013

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…In the analysis of linking human states with health and well-being, fine-grained behavioral indicators can be engineered and generated from the sensory data to provide valuable information of vital biometric responses and behavioral patterns in natural settings [92]. In this vein, Dong et al proposed to use mobile sensing to record people's behavioral footprints during the COVID-19 pandemic and get a better understanding of their collective behavior changes in response to COVID-19 regulations [62]. By using mobile sensing techniques to passively collect behavior related information, students' mobility, activity levels, and communication patterns can be associated with their social anxiety, depression and affect levels [28].…”

Section: Human State Dynamicsmentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

Self Cite

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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Section: Human State Dynamicsmentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, in the long run of mobile sensing data collection, the sensing objects can change their behaviors because of internal and/or external stimuli. Dong et al [48], for example, observe that during COVID-19, people become more sedentary and spend more time at home due to infection risk reduction and public intervention. Therefore, the collected data can come from varying domains, causing domain shift problems that downgrade the generalizability of previously trained models [49].…”

Section: 1mentioning

confidence: 99%

“…In addition, there exists structural topology in noneuclidean domains of raw mobile sensing data (e.g., GPS, Bluetooth Encounter), it can be challenging for handcrafted feature engineering to capture such topological information [52,53]. There are numerous study have been finished in investigating Graph Neural Networks (GNNs) for sensor data, and demonstrated advanced performance in [48,54]. We hypothesize that people who are in early stage of influenza will subconsciously become less active, and avoid unnecessary traveling and person-to-person interactions.…”

Section: Graph Modeling In Mobile Sensingmentioning

confidence: 99%

“…In the modeling of mobile sensing data for influenza-like symptom recognition, by leveraging graph representation of human behaviors as demonstrated in this related works [48,52,55], we transform raw mobile sensing data to graph structured data to extract high-level human behavioral features [56]. We use Graph Convolutional Network (CCN) [57], Graph Attention Network (GAT) [54] and GCN-LSTM [58] to fit the graph structured data, and use Multi-Layer Perceptron (MLP) [59] to fit handcrafted features.…”

Section: Implementation Detailsmentioning

confidence: 99%

See 1 more Smart Citation

Semi-supervised Graph Instance Transformer for Mental Health Inference

Dong

Tang

Cai

et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Mobile sensing appears as a promising solution for health inference problem (e.g., influenza-like symptom recognition) by leveraging diverse smart sensors to capture fine-grained information about human behaviors and ambient contexts. Centralized training of machine learning models can place mobile users' sensitive information under privacy risks due to data breach and misexploitation. Federated Learning (FL) enables mobile devices to collaboratively learn global models without the exposure of local private data. However, there are challenges of on-device FL deployment using mobile sensing: 1) long-term and continuously collected mobile sensing data may exhibit domain shifts as sensing objects (e.g. humans) have varying behaviors as a result of internal and/or external stimulus; 2) model retraining using all available data may increase computation and memory burden; and 3) the sparsity of annotated crowd-sourced data causes supervised FL to lack robustness. In this work, we propose FedMobile, an incremental semi-supervised federated learning algorithm, to train models semi-supervisedly and incrementally in a decentralized online fashion. We evaluate FedMobile using a real-world mobile sensing dataset for influenza-like symptom recognition. Our empirical results show that FedMobile-trained models achieve the best results in comparison to the selected baseline methods.

show abstract

Graph Neural Networks in IoT: A Survey

Dong

Tang²,

Wang³

et al. 2023

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.

show abstract

Detection and Analysis of Interrupted Behaviors by Public Policy Interventions during COVID-19

Cited by 3 publications

References 30 publications

Graph Neural Networks in IoT: A Survey

Graph Neural Networks in IoT: A Survey

Semi-supervised Graph Instance Transformer for Mental Health Inference

Graph Neural Networks in IoT: A Survey

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