@inproceedings{nguyen2025octopinf,

title = {OCTOPINF: Workload-Aware Inference Serving for Edge Video Analytics},

author = {Thanh-Tung Nguyen and Lucas Liebe and Nhat-Quang Tau and Yuheng Wu and Jinghan Cheng and Dongman Lee},

year  = {2025},

date = {2025-03-17},

booktitle = {The 23rd International Conference on Pervasive Computing and Communications (PerCom 2025)},

organization = {IEEE},

abstract = {Edge Video Analytics (EVA) has become a major application of pervasive computing, enabling real-time visual processing. EVA pipelines, composed of deep neural networks (DNNs), typically demand efficient inference serving under stringent latency requirements, which is challenging due to the dynamic Edge environments (e.g., workload variability and network instability). Moreover, EVA pipelines face significant resource contention due to resource (e.g., GPU) constraints at the Edge. In this paper, we introduce OCTOPINF, a novel resource-efficient and workload-aware inference serving system designed for real-time EVA. OCTOPINF tackles the unique challenges of dynamic edge environments through fine-grained resource allocation, adaptive batching, and workload balancing between edge devices and servers. Furthermore, we propose a spatiotemporal scheduling algorithm that optimizes the co-location of inference tasks on GPUs, improving performance and ensuring service-level objectives (SLOs) compliance. Extensive evaluations on a real-world testbed demonstrate the effectiveness of our approach. It achieves an effective throughput increase of up to 10× compared to the baselines and shows better robustness in challenging scenarios. OCTOPINF can be used for any DNN-based EVA inference task with minimal adaptation and is available at https://github.com/tungngreen/PipelineScheduler.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{kim2024metaweather,

title = {MetaWeather: Few-Shot Weather-Degraded Image Restoration},

author = {Youngrae Kim and Younggeol Cho and Thanh-Tung Nguyen and Seunghoon Hong and Dongman Lee},

url = {https://link.springer.com/chapter/10.1007/978-3-031-73464-9_13},

doi = {10.1007/978-3-031-73464-9_13},

isbn = {978-3-031-73464-9},

year  = {2024},

date = {2024-12-04},

urldate = {2024-01-01},

booktitle = {European Conference on Computer Vision},

pages = {206–222},

organization = {Springer},

abstract = {Real-world weather conditions are intricate and often occur concurrently. However, most existing restoration approaches are limited in their applicability to specific weather conditions in training data and struggle to generalize to unseen weather types, including real-world weather conditions. To address this issue, we introduce MetaWeather, a universal approach that can handle diverse and novel weather conditions with a single unified model. Extending a powerful meta-learning framework, MetaWeather formulates the task of weather-degraded image restoration as a few-shot adaptation problem that predicts the degradation pattern of a query image, and learns to adapt to unseen weather conditions through a novel spatial-channel matching algorithm. Experimental results on the BID Task II.A, SPA-Data, and RealSnow datasets demonstrate that the proposed method can adapt to unseen weather conditions, significantly outperforming the state-of-the-art multi-weather image restoration methods. Code is available at https://github.com/RangeWING/MetaWeather.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2024causality,

title = {Causality-Aware Pattern Mining Scheme for Group Activity Recognition},

author = {Hyunju Kim and Heesuk Son and Dongman Lee},

url = {https://ieeexplore.ieee.org/abstract/document/10633339},

doi = {10.1109/COMPSAC61105.2024.00126},

issn = {2836-3795},

year  = {2024},

date = {2024-07-02},

urldate = {2024-01-01},

booktitle = {2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)},

pages = {908–917},

organization = {IEEE},

abstract = {Human activity recognition is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for human activity recognition in smart spaces without privacy and accessibility issues, data streams generated by deployed ambient sensors are leveraged. In this paper, we focus on group activities by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme that extracts causality patterns from ambient sensor event sequences, to support as good recognition accuracy as the state-of-the-art models with missing or false data tolerance. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. Then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern-matching algorithm computes the likelihood of stored group activities to the given test event sequence using matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from real-world testbed and open datasets where users perform their tasks on a daily basis. Experiment results show that the proposed scheme performs higher recognition accuracy and is tolerant to missing or false data with a smaller amount of runtime overhead than the existing schemes.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2024mobile,

title = {Mobile Robot based Personalized Thermal Comfort Scheme},

author = {Hyunju Kim and Geon Kim and Akanksha Jain and Dongman Lee},

url = {https://ieeexplore.ieee.org/abstract/document/10597472},

doi = {10.1109/UR61395.2024.10597472},

isbn = {979-8-3503-6107-0},

year  = {2024},

date = {2024-06-24},

urldate = {2024-06-24},

booktitle = {2024 21st International Conference on Ubiquitous Robots (UR)},

pages = {84–91},

organization = {IEEE},

abstract = {Indoor thermal control is a crucial technique for ensuring user comfort and efficient energy usage, and typically relies on conventional methods that standardize the indoor thermal environment, neglecting individual personalized preferences. Mobile robots have emerged as a potential solution for personalizing temperature comfort. However, the existing research often falls short in considering factors like robot movement control, user activities, human states, and potential disturbance to users, leading to inaccurate estimations of a user's temperature adjustment needs. This paper introduces a mobile robot-based personalized thermal control system, designed to enhance the accuracy in recognizing human states relevant to thermal comfort in real indoor environments. This system achieves accurate thermal comfort estimation using vision-based recognition while reducing robot movement to decrease user inconvenience. The robot dynamically navigates to optimal positions, guided by the confidence level in vision-based human state recognition. We train the robot's movement control policy using a Deep Reinforcement Learning-based model. Real-world evaluation shows the system's success in accurately recognizing human states with minimal movement trajectories and reduced user discomfort. The proposed robot-based approach offers a significant advancement in personalized thermal control, allowing for more accurate thermal comfort estimation.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{han2023enhancing,

title = {Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis},

author = {Sumin Han and Youngjun Park and Minji Lee and Jisun An and Dongman Lee},

url = {https://dl.acm.org/doi/abs/10.1145/3583780.3614867},

doi = {10.1145/3583780.3614867},

isbn = {9798400701245},

year  = {2023},

date = {2023-10-21},

urldate = {2023-01-01},

booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},

pages = {689–698},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {CIKM '23},

abstract = {Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns. Despite making minimal modifications to the conventional graph convolutional recurrent networks and graph convolutional transformer architectures, our approach achieves state-of-the-art performance without introducing excessive computational overhead.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2023exploring,

title = {Exploring Technical Challenges for Thermal Sensation Estimation in Mobile Robot Driven Personalized Thermal Control},

author = {Geon Kim and Hyunju Kim and Youngjae Kim and Dongman Lee},

url = {https://ieeexplore.ieee.org/abstract/document/10202444},

doi = {10.1109/UR57808.2023.10202444},

isbn = {979-8-3503-3517-0},

year  = {2023},

date = {2023-06-25},

urldate = {2023-01-01},

booktitle = {2023 20th International Conference on Ubiquitous Robots (UR)},

pages = {997–1002},

organization = {IEEE},

abstract = {The personalized thermal control studies estimate users' thermal sensations based on wearable or vision-based approaches to control the temperature, humidity, and ventilation in response to individuals' different thermal preferences. However, there is a limitation that both conventional wearable-based and vision-based methods are practical for real-world deployment due to additional equipment, and fixed field of view. This paper discusses the mobile robot-driven human state estimation for personalized thermal control as one of the solutions to overcome these limitations. We investigate the technical issue and its possible solution in designing mobile robot-driven estimation and performed a simulation-based preliminary evaluation to evaluate the effectiveness of a mobile robot on human state estimation. Evaluation results show that the mobile robot can help in the accurate estimation of the user's thermal sensation with only vision data. Finally, we present future directions to further increase the accuracy to achieve wearable-level.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lee2023transformer,

title = {Transformer Based Early Classification for Real-Time Human Activity Recognition in Smart Homes},

author = {Tae-Hoon Lee and Hyunju Kim and Dongman Lee},

url = {https://dl.acm.org/doi/abs/10.1145/3555776.3577693},

doi = {10.1145/3555776.3577693},

isbn = {9781450395175},

year  = {2023},

date = {2023-06-07},

urldate = {2023-01-01},

booktitle = {Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing},

pages = {410–417},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SAC '23},

abstract = {Human activity recognition (HAR) plays a key role in intelligent systems. Ambient sensors are utilized to avoid privacy concerns and to collect data streams in a less intrusive manner in smart homes. In scenarios requiring immediate intervention, the systems must perform HAR in real-time. Since it is difficult to segment the exact transition point in real-time, data unrelated to the target activity can appear at the beginning of the time series, which we call unrefined data. It leads us to a new challenge that the HAR model recognizes a user's activity as early as possible with unrefined data. To explore the impact of unrefined data on real-time HAR, we design an experimental system that consists of a Transformer-based filtering network and an LSTM-based early classifier. We evaluate the experimental system with 3 public datasets collected on testbeds with ambient sensors installed. Our results reveal that unrefined data degrade HAR performance in terms of accuracy and earliness, and the use of the filtering network that filters out unrefined data improves recognition performance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2023towards,

title = {Towards deployment of mobile robot driven preference learning for user-state-specific thermal control in a real-world smart space},

author = {Geon Kim and Hyunju Kim and Dongman Lee},

url = {https://dl.acm.org/doi/abs/10.1145/3555776.3577760},

doi = {10.1145/3555776.3577760},

isbn = {9781450395175},

year  = {2023},

date = {2023-06-07},

urldate = {2023-01-01},

booktitle = {Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing},

pages = {724–731},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SAC '23},

abstract = {Indoor Environment Quality (IEQ) is one of the most important goals for smart spaces. Thermal comfort is typically considered the most emphasized factor in IEQ that depends on personalized thermal preference. In this paper, we explore technical challenges to deploying a robot-driven personalized thermal control system that uses a mobile robot for learning user-state-specific preference efficiently. We conduct a few experiments that give a clue to overcome such challenges (i.e. low image recognition) when the system is deployed in a real world. We present future directions to improve robot-driven preference learning from the exploration.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nguyen2023preacto,

title = {PreActo: Efficient Cross-Camera Object Tracking System in Video Analytics Edge Computing},

author = {Thanh-Tung Nguyen and Si Young Jang and Boyan Kostadinov and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/10099298},

doi = {10.1109/PERCOM56429.2023.10099298},

issn = {2474-249X},

year  = {2023},

date = {2023-03-13},

urldate = {2023-03-13},

booktitle = {2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)},

pages = {101--110},

organization = {IEEE},

abstract = {Cross-camera real-time object tracking is one of the important, yet challenging applications of video analytics in edge computing environments. To provide accurate and efficient real-time tracking, a tracking target's future movements need to be predicted. Particularly, the destination camera and travel time of the target object are to be identified so that tracking duties can be handover-ed seamlessly. In this paper, we propose a collaborative cross-camera tracking system, called PreActo, with two key features: (1) ResNet-based trajectory learning to exploit the rich spatio-temporal information embedded within objects' moving patterns, which has not been utilized by the existing literature, and (2) collaboration between the edge server and the edge device for real-time trajectory prediction and tracking handover. To prove the validity of our proposed system, we evaluate PreActo on a video dataset leveraging real-world trajectories. Evaluation results show that the proposed system reduces up to 7× the number of processed frames for handover, with 2× lower latency while providing 1.5× tracking precision improvement compared to the state-of-the-art.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2023efficient,

title = {Efficient reference-based video super-resolution (ervsr): Single reference image is all you need},

author = {Youngrae Kim and Jinsu Lim and Hoonhee Cho and Minji Lee and Dongman Lee and Kuk-Jin Yoon and Ho-Jin Choi},

url = {https://ieeexplore.ieee.org/document/10030311},

doi = {10.1109/WACV56688.2023.00187},

issn = {2642-9381},

year  = {2023},

date = {2023-01-02},

urldate = {2023-01-01},

booktitle = {2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

pages = {1828–1837},

organization = {IEEE},

abstract = {Reference-based video super-resolution (RefVSR) is a promising domain of super-resolution that recovers high-frequency textures of a video using reference video. The multiple cameras with different focal lengths in mobile devices aid recent works in RefVSR, which aim to super-resolve a low-resolution ultra-wide video by utilizing wide-angle videos. Previous works in RefVSR used all reference frames of a Ref video at each time step for the super-resolution of low-resolution videos. However, computation on higher-resolution images increases the runtime and memory consumption, hence hinders the practical application of RefVSR. To solve this problem, we propose an Efficient Reference-based Video Super-Resolution (ERVSR) that exploits a single reference frame to super-resolve whole low-resolution video frames. We introduce an attention-based feature align module and an aggregation upsampling module that attends LR features using the correlation between the reference and LR frames. The proposed ERVSR achieves 12xfaster speed, 1/4 memory consumption than previous state-of-the-art RefVSR networks, and competitive performance on the RealMCVSR dataset while using a single reference image.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2022correlationb,

title = {A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities},

author = {Kisoo Kim and Hyunju Kim and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/9842485},

doi = {10.1109/COMPSAC54236.2022.00150},

isbn = {978-1-6654-8810-5},

year  = {2022},

date = {2022-08-10},

urldate = {2022-08-10},

booktitle = {2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)},

pages = {972–981},

organization = {IEEE},

abstract = {Activity Segmentation, dividing a continuous sensor stream into a set of activity segments, is a crucial pre-process in Human Activity Recognition (HAR) and it is required to be done in real-time for real-world smart services. Existing single-user activity segmentation schemes fail to correctly detect transition points due to concurrent and overlapping events from multiple users in case of Multi-user Collaborative Activity Recognition (MCAR). In this paper, we propose a novel scheme for activity segmentation for MCAR that expresses complex events and the correlations between them. For this, the proposed scheme first creates an event stream from a sensor stream and defines event sets in terms of time windows. For each time window, two types of correlations for every event pair are calculated: duration correlation and history correlation. After calculating event correlation, the change score of a time window is measured by comparing the calculated correlation values with those of the preceding windows. Then, the proposed scheme elects as an activity transition point a time window whose change score exceeds the transition threshold. We evaluate the proposed method on two multi-user collaborative activity datasets and experiment results show that the proposed scheme achieves better segmentation performance than existing approaches.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2022correlation,

title = {A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities},

author = {Kisoo Kim and Hyunju Kim and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/9842485},

doi = {10.1109/COMPSAC54236.2022.00150},

isbn = {978-1-6654-8810-5},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)},

pages = {972–981},

organization = {IEEE},

abstract = {Activity Segmentation, dividing a continuous sensor stream into a set of activity segments, is a crucial pre-process in Human Activity Recognition (HAR) and it is required to be done in real-time for real-world smart services. Existing single-user activity segmentation schemes fail to correctly detect transition points due to concurrent and overlapping events from multiple users in case of Multi-user Collaborative Activity Recognition (MCAR). In this paper, we propose a novel scheme for activity segmentation for MCAR that expresses complex events and the correlations between them. For this, the proposed scheme first creates an event stream from a sensor stream and defines event sets in terms of time windows. For each time window, two types of correlations for every event pair are calculated: duration correlation and history correlation. After calculating event correlation, the change score of a time window is measured by comparing the calculated correlation values with those of the preceding windows. Then, the proposed scheme elects as an activity transition point a time window whose change score exceeds the transition threshold. We evaluate the proposed method on two multi-user collaborative activity datasets and experiment results show that the proposed scheme achieves better segmentation performance than existing approaches.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cho2021dnn,

title = {Dnn model deployment on distributed edges},

author = {Eunho Cho and Juyeon Yoon and Daehyeon Baek and Dongman Lee and Doo-Hwan Bae},

url = {https://link.springer.com/chapter/10.1007/978-3-030-92231-3_2},

doi = {10.1007/978-3-030-92231-3_2},

isbn = {978-3-030-92230-6},

year  = {2021},

date = {2021-12-08},

urldate = {2021-12-08},

booktitle = {International Conference on Web Engineering},

pages = {15–26},

organization = {Springer},

abstract = {Deep learning-based visual analytic applications have drawn attention by suggesting fruitful combinations with Deep Neural Network (DNN) models and visual data sensors. Because of the high cost of DNN inference, most systems adopt offloading techniques utilizing a high-end cloud. However, tasks that require real-time streaming often suffer from the problem of an imbalanced pipeline due to the limited bandwidth and latency between camera sensors and the cloud. Several DNN slicing approaches show that effectively utilizing the edge computing paradigm effectively lowers the frame drop rate and overall latency, but recent research has primarily focused on building a general framework that only considers a few fixed settings. However, we observed that the optimal split strategy for DNN models can vary significantly based on application requirements. Hence, we focus on the characteristics and explainability of split points derived from various application goals. First, we propose a new simulation framework for flexible software-level configuration, including latency and bandwidth, using dockercompose, and we experiment on a 14-layered Convolutional Neural Network (CNN) model with diverse layer types. We report the results of the total process time and frame drop rate of 50 frames with three different configurations and further discuss recommendations for providing proper decision guidelines on split points, considering the target goals and properties of the CNN layers.



},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2021adela,

title = {ADELA: attention based deep ensemble learning for activity recognition in smart collaborative environments},

author = {Hyunju Kim and Dongman Lee},

url = {https://dl.acm.org/doi/10.1145/3412841.344192},

doi = {10.1109/COMPSAC54236.2022.00150},

isbn = {9781450381048},

year  = {2021},

date = {2021-04-22},

urldate = {2021-04-22},

booktitle = {Proceedings of the 36th Annual ACM Symposium on Applied Computing},

pages = {436–444},

abstract = {Human activity recognition (HAR) is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. For HAR in a smart environment without privacy and accessibility issues, data streams generated by environmental sensors are leveraged. In this paper, we propose ADELA, a novel activity recognition scheme in a smart collaborative environment where a group of users performs an activity without user identification. ADELA calculates the importance of events from sensor data streams depending on their impact on recognizing activities and finds the best activity recognition base models using attention-based ensemble learning. After the training phase, each base model obtains its weight through weighted majority calculation, and ADELA stores the information to Trained Model Storage to reuse it for inferring. We evaluate ADELA using the data collected from our testbed and CASAS dataset where users perform their tasks daily and validate the effectiveness of ADELA in a real environment. Experiment results show that the proposed scheme performs higher recognition performance than existing approaches.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lee2021dqn,

title = {DQN-based coverage maximization for mobile video camera networks},

author = {Seungho Lee and Si Young Jang and Soon J Hyun and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/9369618},

doi = {10.1109/CCNC49032.2021.9369618},

isbn = {978-1-7281-9794-4},

year  = {2021},

date = {2021-03-11},

urldate = {2021-01-01},

booktitle = {2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)},

pages = {01–02},

organization = {IEEE},

abstract = {Coverage maximization is an important issue of Mobile wireless sensor networks (M-WSN). Especially for visual sensors like video camera which have a specific sensing direction and range, obstacles and the position of the sensors should also be considered. In this paper, we propose an efficient coverage maximization method for mobile video camera networks by leveraging DQN, a deep reinforcement learning algorithm. Evaluation results show that the proposed method can cover up to 4.93% better than existing ones.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jang2021microservice,

title = {Microservice-based edge device architecture for video analytics},

author = {Si Young Jang and Boyan Kostadinov and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/9709018},

doi = {10.1145/3453142.3491283},

isbn = {978-1-4503-8390-5},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE/ACM Symposium on Edge Computing (SEC)},

pages = {165–177},

organization = {IEEE Computer Society},

abstract = {With today's ubiquitous deployment of video cameras and other edge devices, progress in edge computing is happening at an incredible speed. Yet, one aspect of real-time video analytics at the edge that is still underdeveloped is the support for processing multitenant, multi-application scenarios with a limited set of resources. Existing systems either fail to provide the necessary performance, or rely too heavily on edge or cloud servers to handle the workload. This work proposes a new approach, inspired by both Function-as-a-Service and microservices architecture in order to efficiently place and execute video analytics pipelines on edge devices. The main contributions of this work are the ability to dynamically add and run new applications on already deployed systems, and the capability to horizontally distribute pipelines across other neigh-bouring edge devices. We prototype an implementation that we evaluate using multiple concurrent applications per device. Results show that our system provides more flexibility for on-the-fly re-configuration than existing works do, with 20 % improvement in latency and 3.9 X increase in throughput.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2021ar,

title = {AR-T: Temporal relation embedded transformer for the real world activity recognition},

author = {Hyunju Kim and Dongman},

url = {https://link.springer.com/chapter/10.1007/978-3-030-94822-1_40},

doi = {10.1007/978-3-030-94822-1_40},

isbn = {978-3-030-94821-4},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services},

pages = {617–633},

organization = {Springer},

abstract = {Activity recognition is a fundamental way to support context-aware services for users in smart spaces. Data sources such as video or wearable devices are used in many recognition approaches, but there are challenges in utilizing them in the real world. Recent approaches propose deep learning-based methods on IoT sensor data streams to overcome the issues. Since they only describe single user-based spaces, they are vulnerable to complex sequences of events triggered by multiple users. When multiple users exist in a space, various overlapping events occur with longer correlations than a single user situation. Additionally, ambient sensor-based events appear far more than actuator-based events, making it difficult to extract actuator-based events as important features. We propose a transformer-based approach to derive long-term event correlations and important events as elements of activity patterns. We also develop a duration incorporated embedding method to differentiate between the same type but different duration events and add a sequential manner to the transformer approach. In the experiments section, we prove that our approach outperforms the existing approaches based on real datasets.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2021tap,

title = {TAP: A Transformer based Activity Prediction Exploiting Temporal Relations in Collaborative Tasks},

author = {Hyunju Kim and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/9431008},

doi = {10.1109/PerComWorkshops51409.2021.9431008},

isbn = {978-1-6654-0424-2},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},

organization = {IEEE},

abstract = {Activity prediction is an important challenge to provide cognitive supports in a smart space. For user activity prediction without privacy and inconsistent data collection issues, recent studies leverage data stream generated by ambient sensors. They use a deep learning model for single-user tasks which only contain simple sequential relations between activities. In this paper, we propose TAP, a Transformer-based activity prediction approach for inferring the next activity in a collaborative smart environment where collaborative tasks are conducted. To represent and analyze complex relations between activities of users, Allen's temporal relations are employed for representing temporal relations between activities and we leverage the Transformer to predict not only the next activity but also temporal relation with its preceding activity. TAP yields higher accuracy for the next activity prediction and temporal relations with the current activity in a collaborative task than existing approaches.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kim2020human,

title = {Human Activity Recognition Using Semi-supervised Multi-modal DEC for Instagram Data},

author = {Dongmin Kim and Sumin Han and Heesuk Son and Dongman Lee},

url = {https://dl.acm.org/doi/10.1007/978-3-030-47426-3_67},

doi = {10.1007/978-3-030-47426-3_67},

isbn = {978-3-030-47425-6},

year  = {2020},

date = {2020-05-11},

urldate = {2020-05-11},

booktitle = {Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part I 24},

pages = {869–880},

organization = {Springer},

abstract = {Human Activity Recognition (HAR) using social media provides a solid basis for a variety of context-aware applications. Existing HAR approaches have adopted supervised machine learning algorithms using texts and their meta-data such as time, venue, and keywords. However, their recognition accuracy may decrease when applied to image-sharing social media where users mostly describe their daily activities and thoughts using both texts and images. In this paper, we propose a semi-supervised multi-modal deep embedding clustering method to recognize human activities on Instagram. Our proposed method learns multi-modal feature representations by alternating a supervised learning phase and an unsupervised learning phase. By utilizing a large number of unlabeled data, it learns a more generalized feature distribution for each HAR class and avoids overfitting to limited labeled data. Evaluation results show that leveraging multi-modality and unlabeled data is effective for HAR and our method outperforms existing approaches.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yang2020context,

title = {Context-aware trust estimation for realtime crowdsensing services in vehicular edge networks},

author = {Hongmin Yang and Jin-Hee Cho and Heesuk Son and Dongman Lee},

url = {https://ieeexplore.ieee.org/abstract/document/9045221},

doi = {10.1109/CCNC46108.2020.9045221},

isbn = {978-1-7281-3893-0},

year  = {2020},

date = {2020-03-26},

urldate = {2020-01-01},

booktitle = {2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)},

pages = {1–6},

organization = {IEEE},

abstract = {This work proposes a context-aware trust estimation scheme that can allow roadside units in a vehicular edge network to provide real-time crowdsensing services in a reliable manner by selectively using information from trustworthy sources. Our proposed scheme is novel in that its trust estimation does not require any prior knowledge towards vehicles on roads but quickly obtains an accurate trust value of each vehicle. To that end, we particularly leverage the concept of I-sharing which removes a cold-start problem during the system bootstrapping period. Based on an extensive simulation study, we prove that the proposed scheme outperforms its competitive counterpart and baseline models in terms of trust bias and malicious vehicle detection accuracy.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{han2020exploring,

title = {Exploring commercial gentrification using Instagram data},

author = {Sumin Han and Dasom Hong and Dongman Lee},

url = {https://ieeexplore.ieee.org/abstract/document/9381374},

doi = {10.1109/ASONAM49781.2020.9381374},

isbn = {978-1-7281-1056-1},

year  = {2020},

date = {2020-03-24},

urldate = {2020-03-24},

booktitle = {2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},

pages = {557–564},

organization = {IEEE},

abstract = {Commercial gentrification refers to the replacement of low-value businesses like small local stores into high-value businesses like boutiques and chain stores. A handful of research efforts have been made to identify gentrification and their change by leveraging social media. However, their approaches lack in inferring how much commercial gentrification is developed in a target area and how long it has taken for the area to get to that phase. In this paper, we propose a novel scheme to estimate the commercial gentrification status of a target area and its development in terms of time and geographic dispersion using Instagram data. For this, we define our commercial gentrification phase criteria based on the conceptual model from the urban study. Then, we extract social features from both images and texts of Instagram posts, and leverage regression models to infer the commercial gentrification phase of a target area at the monthly timestamp. We also measure how geographical dispersion of geo-tagged Instagram posts matches the boutiques, which is the physical variable that has the strongest correlation with the commercial gentrification. Evaluation results show that our method yields a good quality of estimation compared to the ground truth. This assures that our method could be a meaningful tool for urban planners and policymakers to investigate and manage commercial gentrification.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{han2020discovering,

title = {Discovering Daily POI Exploitation Using LTE Cell Tower Access Traces in Urban Environment},

author = {Sumin Han and Kinam Park and Dongman Lee},

url = {https://link.springer.com/chapter/10.1007/978-3-030-60975-7_7},

doi = {10.1007/978-3-030-60975-7_7},

isbn = {978-3-030-60974-0},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {International Conference on Social Informatics},

pages = {81–94},

organization = {Springer},

abstract = {Point of interest (POI) in an urban space represents the perception of city dwellers and visitors of a certain place. LTE cell tower access trace data is one of the promising data sources which has the potential to show real-time POI exploitation analysis. However, there is not much discussion on how it is correlated to diachronic POIs and their exploitation pattern. In this paper, we first show that the access trace pattern from the LTE cell tower can be used to discover which types of POIs exist in a certain area. Then, we propose a daily POI exploitation discovery scheme which can extract patterns of how POIs are daily used. Our analysis can provide a good insight into future urban space-based services such as urban planning and tourism.



},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{son2019distributed,

title = {Distributed multi-agent preference learning for an IoT-enriched smart space},

author = {Heesuk Son and Jeongwook Park and Hyunju Kim and Dongman Lee},

url = {https://ieeexplore.ieee.org/document/8884817},

doi = {10.1109/ICDCS.2019.00206},

isbn = {978-1-7281-2519-0},

year  = {2019},

date = {2019-10-31},

urldate = {2019-01-01},

booktitle = {2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)},

pages = {2090–2100},

organization = {IEEE},

abstract = {There have been several efforts on preference learning in a smart space by means of multi-agent collaborations. Each agent captures a user action or handles part of learning but decision makings are done in a centralized manner. This makes it difficult for a smart space to deal with learning complexity due to the increase and reconfiguration of smart devices. While the complexity is relieved by articulating the learning space, it is not flexible because the articulation procedure needs to be resumed whenever a smart space reconfiguration occurs. In this paper, we propose a distributed multi-agent preference learning architecture which allows a group of physically separate agents to collaborate with each other for learning a user's task preference efficiently in an IoT enriched smart space. For this, the proposed scheme provides four key features: ontology-based knowledge structure for task-driven agent collaboration, knowledge exchange protocol for task-aware causality among agents, Q-learners for observing and learning from user behaviors, and negotiation and acknowledgement protocol for preventing agents from performing disorganized actions. Evaluation results show that the proposed scheme allows smart device agents to learn user preferences in a fully distributed way and outperforms existing approaches in terms of learning speed and system overhead.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jang2018towards,

title = {Towards application-aware virtualization for edge iot clouds},

author = {Si Young Jang and Yoonhyung Lee and Byoungheon Shin and Dongman Lee},

url = {https://dl.acm.org/doi/abs/10.1145/3226052.3226059},

doi = {https://doi.org/10.1145/3226052.3226059},

year  = {2018},

date = {2018-06-20},

urldate = {2018-01-01},

booktitle = {Proceedings of the 13th International Conference on Future Internet Technologies},

pages = {1–4},

abstract = {We explore issues and challenges for application-aware virtualization in edge Internet-of-Things (IoT) clouds. Since a large number of IoT devices reside in network edges, it is necessary for a current edge computing technology to virtualize them not only as computational resources but also functional resources for achieving the goal of a specific service, which we call application-aware virtualization. This requires translation of abstract application requirements into IoT resource allocation policies using the knowledge of relationships between application layer semantics and underlying device functionalities and status. We implement a prototype for proof-of-concept and show that it is able to dynamically allocate and reallocate IoT resources to applications according to the application's requirements and contexts changes with a reasonably small processing overhead.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jang2017semantic,

title = {Semantic virtualization for edge-IoT cloud: Issues and challenges},

author = {Si Young Jang and Hayoung Choi and Yoonhyung Lee and Byoungheon Shin and Dongman Lee},

url = {https://dl.acm.org/doi/abs/10.1145/3155921.3157052},

doi = {https://doi.org/10.1145/3155921.3157052},

year  = {2017},

date = {2017-12-11},

urldate = {2017-01-01},

booktitle = {Proceedings of the 2nd Workshop on Cloud-Assisted Networking},

pages = {55–60},

abstract = {It is well expected that a collection of smart objects such as IoT devices dynamically form an edge cloud allowing acquiring, storing, communicating, and processing of information done at the edge of the network. Edge cloud workers (i.e. IoT devices) usually have service specific characteristics while legacy cloud workers are more computing oriented. However, existing cloudlet architectures continue to take their allocation policy centered around computation resource virtualization without considering such characteristics of edge IoT clouds. In order to provide service specific QoS driven virtualization of an edge IoT cloud, which we call semantic virtualization, a new approach is required. In this paper, we investigate research issues and directions for realizing semantic virtualization for an edge IoT cloud.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{park2017don,

title = {Don't bother me. I'm socializing! A breakpoint-based smartphone notification system},

author = {Chunjong Park and Junsung Lim and Juho Kim and Sung-Ju Lee and Dongman Lee},

url = {https://dl.acm.org/doi/abs/10.1145/2998181.2998189},

doi = {https://doi.org/10.1145/2998181.2998189},

year  = {2017},

date = {2017-02-25},

urldate = {2017-01-01},

booktitle = {Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing},

pages = {541–554},

abstract = {Smartphone notifications provide application-specific information in real-time, but could distract users from in-person social interactions when delivered at inopportune moments. We explore breakpoint-based notification management, in which the smartphone defers notifications until an opportune moment. With a video survey where participants selected appropriate moments for notifications from a video-recorded social interaction, we identify four breakpoint types: long silence, a user leaving the table, others using smartphones, and a user left alone. We introduce a Social Context-Aware smartphone Notification system, SCAN, that uses build-in sensors to detect social context and identifies breakpoints to defer smartphone notifications until a breakpoint. We conducted a controlled study with ten friend groups who had SCAN installed on their smartphones while dining at a restaurant. Results show that SCAN accurately detects breakpoints (precision=92.0%, recall=82.5%), and reduces notification interruptions by 54.1%. Most participants reported that SCAN helped them to focus better on in-person social interaction and found selected breakpoints appropriate.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{shin2016efficient,

title = {An efficient MAC layer packet fragmentation scheme with priority queuing for real-time video streaming},

author = {Byoungheon Shin and Jalil Abdullayev and Dongman Lee},

url = {https://ieeexplore.ieee.org/abstract/document/7796764},

doi = {10.1109/LCN.2016.18},

isbn = {978-1-5090-2054-6},

year  = {2016},

date = {2016-12-26},

urldate = {2016-12-26},

booktitle = {2016 IEEE 41st Conference on Local Computer Networks (LCN)},

pages = {69–77},

organization = {IEEE},

abstract = {This paper proposes a novel priority-aware packet fragmentation extension to high throughput WLANs such as IEEE 802.11n for streaming of H264/AVC encoded videos. Unlike existing fragmentation schemes, the proposed scheme fragments IP packets based on the priority of video packets and the characteristics of MPEG-2 TS, where the original IP packet is fragmented into smaller IP packets containing fewer TS packets and prioritizes individual TS packets, allocated to an appropriate priority queue. The proposed scheme is evaluated on a testbed with various network congestion levels and channel conditions. The results show that the proposed scheme achieves higher quality of the streaming video in terms of PSNR than existing schemes as the network congestion level and the bit error rate increase.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}