-
- Modeling and Simulation Lab - Prof. TAE HO CHO
-
- TAE HO CHO
- https://modsim.skku.ac.kr
Major research interests
Digital Twin for monitoring and action execution
Intelligent Spatiotemporal Computation
Modeling and Simulation
Context aware architecture
Major research achievements (2020~present)
Jung Sub Ahn and Tae Ho Cho, "Modeling and Simulation of Abnormal Behavior Detection through History Trajectory Monitoring in Wireless Sensor Networks," IEEE Access, Vol.10, pp. 119232-119243 Aug. 2022.
Won Jin Chung and Tae Ho Cho, "Complex attack detection scheme using history trajectory in internet of vehicles," Egyptian Informatics Journal, Elsevier, vol. 23 (3), pp. 499-510, Sep. 2022.
Tae Ho Cho, "ST-DEVS: A Methodology Using Time-Dependent-Variable-Based Spatiotemporal Computation," Symmetry-Basel, Vol.14 (5), 912, Apr. 2022.
Jung Sub Ahn & Sang Hyeok Lim and Tae Ho Cho. "Fuzzy Logic-based Efficient Message Route Selection Method to Prolong the Network Lifetime in WSNs" International Journal of Computer Networks & Communications (IJCNC), Vol. 13, No. 6, pp. 73 - 91, Nov. 2021.
Ye Lim Kang & Tae Ho Cho. "Simulation of A Fine Dust Value-Based False Data Detection System To Improve Security In WSN-Based Air Purification IoT" International Journal of Engineering and Advanced Technology (IJEAT) , Vol. 8, No. 81, pp. 945 - 956, Aug. 2021.
Won Jin Chung & Tae Ho Cho. "A security scheme based on blockchain and a hybrid cryptosystem to reduce packet loss in IoV" International Journal of Advanced Technology and Engineering Exploration(IJATEE) , Vol. 8, No. 81, pp. 945 - 956, Aug. 2021.
Ga-Hyeon An & Tae-Ho Cho. "Wormhole Detection Using Encrypted Node IDs and Hop Counts in the Event Report of Statistical En-route Filtering" International Journal of Computer Networks and Applications (IJCNA) , Vol. 8, No. 4, pp. 390 - 399, Aug. 2021.
Tae Ho Cho, "Simulation Methodology-Based Context-Aware Architecture Design for Behavior Monitoring of Systems," Symmetry-Basel, Vol.12 (9), 1568, Sep. 2020.
Su Man Nam and Tae Ho Cho, "Discrete event simulation–based energy efficient path determination scheme for probabilistic voting–based filtering scheme in sensor networks," International Journal of Distributed Sensor Networks, vol. 16 (8), 1-13 Aug. 2020.
-
- Internet of Things (IoT) Lab - Prof. JAEHOON JEONG
-
- JAEHOON JEONG
- http://iotlab.skku.edu/publications.php
Major research interests
Cloud-Based Security Systems - Interface to Network Security Functions (I2NSF): https://datatracker.ietf.org/wg/i2nsf/documents/
Vehicular Networking - IPv6 Wireless Access in Vehicular Environments (IPWAVE):
https://datatracker.ietf.org/wg/ipwave/documents/
SNS Influencer Marketing Platform: https://ieeexplore.ieee.org/document/9728965
Indoor Positioning Systems: https://ieeexplore.ieee.org/document/9296763
Major research achievements (2020~present)
“Context-Aware Navigation Protocol for Safe Driving in Vehicular Cyber-Physical Systems”, IEEE Transactions on Intelligent Transportation Systems, CNP supplemental material, Vol. 24, No. 1, pp. 128-138, January 2023.
“A comprehensive survey on data dissemination in Vehicular Ad Hoc Networks”, Elsevier Vehicular Communications, Vol. 34, April 2022.
“A comprehensive survey on vehicular networking for safe and efficient driving in smart transportation: A focus on systems, protocols, and applications”, Elsevier Vehicular Communications, Vol. 31, October 2021.
“A comprehensive survey on vehicular networks for smart roads: A focus on IP-based approaches”, Elsevier Vehicular Communications, Vol. 29, June 2021.
“DFC: Device-Free Human Counting through Wi-Fi Fine-Grained Subcarrier Information”, IET Communications, Vol. 15, Issue 3, pp. 337–350, January 2021.
“Particle Filtering-Based Indoor Positioning System for Beacon Tag Tracking”, IEEE Access, December 2020.
“IBCS: Intent-Based Cloud Services for Security Applications”, IEEE Communications Magazine, Vol. 58, No. 4, pp. 45-51, April 2020.
“DAPF: Delay-Aware Packet Forwarding for Driving Safety and Efficiency in Vehicular Networks”, IET Communications, January 2020.
-
- ING Lab - Prof. YUN-GYUNG CHEONG
-
- YUN-GYUNG CHEONG
- https://inglab.github.io/
Major research interests
Story Comprehension and Generation
Natural Language Processing
Text Analysis, Data Mining
Major research achievements (2019~Present)
Genre-Controllable Story Generation via Supervised Contrastive Learning. The ACM Web Conference 2022
Analysis of Autoencoders for Network Intrusion Detection. Sensors. 2021
Extracting and Clustering of Story Events from a Story Corpus. KSII Transactions on Internet & Information Systems 2021
The CreativeSumm 2022 Shared Task: A Two-Stage Summarization Model using Scene Attributes. The Workshop on Automatic Summarization for Creative Writing (ACL) 2022
A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization, Swarm and Evolutionary Computation. 2021
Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization. IEEE Access. 2020
Research Projects
Basic Science Research Program (2019-2023, NRF): Research on Text Understanding Techniques for Intelligent Story Generation Systems
ETRI (’20-’23, Ministry of Science and ICT - IITP) – Development of 5G Edge Security Technology for Ensuring 5G+ Service Stability and Availability
NCSoft (‘21-’22) - Analysis of Story Structure
SKKU Convergence Research Fund (’20, Collaboration with Samsung Hospital) - Development of an Ophthalmologist Chatbot
ICT Information Technology Research Center Support Program (’17-’22, Ministry of Science and ICT - IITP) - Content Creation Technology Utilizing Artificial Intelligence
-
- Systems Security Lab (SSLab) - Prof. HOJOON LEE
-
Major research interests
Software sandboxing and compartmentalization
Hardware-based/assisted software security
Confidential and Oblivious Computing
Selected Publications
DID We Miss Anything?: Towards Privacy-Preserving Decentralized ID Architecture. Siwon Huh, Myungkyu Shim, Jihwan Lee, Simon Woo, Hyoungshick Kim, Hojoon Lee. IEEE Transactions on Dependable and Secure Computing (TDSC) (2023)
SE-PIM: In-Memory Acceleration of Data-Intensive Confidential Computing. Kha Dinh Duy, Hojoon Lee. IEEE Transactions on Cloud Computing (TDSC) (2022)
Harnessing the x86 Intermediate Rings for Intra-Process Isolation. Hojoon Lee, Chihyun Song, Brent Byunghoon KanIEEE Transactions on Dependable and Secure Computing (TDSC) (2022)
Lord of the x86 Rings: A Portable User Mode Privilege Separation Architecture on x86. Hojoon Lee, Chihyun Song, and Brent Byunghoon Kang. ACM CCS 2018
ATRA: Address Translation Redirection Attack Against Hardware-based External Monitors. Dahee Jang, Hojoon Lee, Hyungon Moon, Minsu Kim, Daehyeok Kim, Daegyeong Kim, Brent Byunghoon Kang. ACM CCS 2014
KI-Mon: A Hardware-assisted Event-triggered Monitoring Platform for Mutable Kernel Object. Hojoon Lee, Hyungon Moon, Daehee Jang, Kihwan Kim, Jihoon Lee, Yunheung Paek, Brent Byunghoon Kang. USENIX Security 2013
Vigilare: Toward Snoop-based Kernel Integrity Monitor. Hyungon Moon, Hojoon Lee, Jihoon Lee, Kihwan Kim, Yunheung Paek, Brent Bynghoon KanACM CCS 2012
KI-Mon ARM: A Hardware- assisted Event-triggered Monitoring Platform for Mutable Kernel ObjectHojoon Lee, Hyungon Moon, Daehee Jang, Kihwan Kim, Jihoon Lee, Yunheung Paek, Brent Byunghoon Kang. IEEE Transactions on Dependable and Secure Computing (TDSC), pages 1–1, 2018
-
- Real-Time Computing Lab - Prof. JINKYU LEE
-
- JINKYU LEE
- https://rtclskku.github.io/website/
Major Research Interests
Real-Time Scheduling and Systems
-Machine Learning for Real-Time & Real-Time for Machine Learning
Software Defined Batteries
Mobile Computing and Systems
Major Research Achievements (2021~present)
“Tight Necessary Feasibility Analysis for Recurring Real-Time Tasks on a Multiprocessor,” Journal of Systems Architecture, Feb. 2023
"Response Time Analysis for Real-Time Global Gang Scheduling,” IEEE Real-Time Systems Symposium (RTSS), Dec. 2022
“Design and Timing Guarantee for Non-Preemptive Gang Scheduling,” IEEE Real-Time Systems Symposium (RTSS), Dec. 2022
“RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks,” IEEE Real-Time Systems Symposium (RTSS), Dec. 2022
“MC-FLEX: Flexible Mixed-Criticality Real-Time Scheduling by Task-Level Mode Switch,” IEEE Transactions on Computers, Aug. 2022
“Schedulability Performance Improvement via Task Split in Real-Time Systems,” Journal of Systems Architecture, Aug. 2022
“Necessary Feasibility Analysis for Mixed-Criticality Real-Time Embedded Systems,” IEEE Transactions on Parallel and Distributed Systems, July. 2022
“TherMobile: Measuring Body Temperature Using a Mobile Device,” IEEE Sensors Journal, July 2022.
“DNN-SAM: Split-and-Merge DNN Execution for Real-Time Object Detection,” IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), May 2022
“LaLaRAND: Flexible Layer-by-Layer CPU/GPU Scheduling for Real-Time DNN Tasks,” IEEE Real-Time Systems Symposium (RTSS), Dec. 2021
“ML for RT: Priority Assignment Using Machine Learning,” IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), May 2021
-
- Information and Intelligence Lab - Prof. JEEHYONG LEE
-
- JEEHYONG LEE
- https://iislab.skku.edu
Major research interests
Self-supervised Learning, Semi-supervised Learning, Federated Learning
Object Detection & Segmentation, Domain Adaptation/Generalization
Program Code Summarization, Code/LM Attack
Medical Image Analysis, Industrial Application of AI/ML
Major research achievements (2015~present)
TABS: Efficient Textual Adversarial Attack for Pre-trained NL Code Model Using Semantic Beam Search, EMNLP, 2022
Propagation Regularizer for Semi-supervised Learning with Extremely Scarce Labeled Samples, CVPR, 2022
IA-BERT: Context-Aware Sarcasm Detection by Incorporating Incongruity Attention Layer for Feature Extraction, ACM-SAC, 2022
Learning Sequential and Structural Information for Source Code Summarization, ACL-IJCNLP, 2021
An embedding method for unseen words considering contextual information and morphological information, ACM-SAC, 2021
Attention History-based Attention for Abstractive Text Summarization, ACM-SAC, 2020
An E-sports video highlight generator using win-loss probability model, ACM-SAC, 2020
Autonomic machine learning platform, Information Management, 2018
Feature Selection for High Dimensional Data using Monte Carlo Tree Search, IEEE Access, 2018
A novel recommendation approach based on chronological cohesive units in content consuming logs, Information Sciences, 2018
Accurate lithography hotspot detection using deep convolutional neural networks, JM3, 2016
An Approach for Multi-Label Classification by Directed Acyclic Graph with Label Correlation Maximization, Information Sciences, 2016
Noisy and incomplete fingerprint classification using local ridge distribution models, Pattern Recognition, 2015
-
- Computer Systems & Intelligence (CSI) Lab - Prof. JOONWON LEE
-
- JOONWON LEE
- http://csl.skku.edu/People/Joon
Research interests
Operating Systems
Cloud Computing
Storage System
Embedded Computing
Selected Publications
Transparently Exploiting Device-reserved Memory for Application Performance in Mobile Systems, IEEE Transactions on Mobile Computing, vol. 15, no. 11, pp. 2878-2891, Nov 2016, Jinkyu Jeong, Hwanju Kim, and Joonwon Lee
Cache Scheme of Shared-Buffer Mappings for Energy-Efficiency of Mobile Devices, IET Electronics Letters, vol. 51, no. 11, pp. 830-832, May 2015, Jinkyu Jeong, Joonwon Lee and Euiseong Seo
Exploiting Asymmetric CPU Performance for Fast Startup of Subsystem in Mobile Smart Devices, IEEE Transactions on Consumer Electronics, vol. 61, no. 1, pp. 103-111, Feb. 2015, Jun Kim, Joonwon Lee, and Jinkyu Jeong
Virtual Asymmetric Multiprocessor for Interactive Performance of Consolidated Desktops, ACM SIGPLAN Notices, vol. 49, no. 7, pp. 29-40, Jul. 2014, Hwanju Kim, Sangwook Kim, Jinkyu Jeong, and Joonwon Lee.
Sung-hun Kim, Jinkyu Jeong, and Joonwon Lee, "Selective Memory Deduplication for Cost Efficiency in Mobile Smart Devices," IEEE Transactions on Consumer Electronics, Vol. 60, Issue 2, pp. 276-284, May 2014
Group-based Memory Oversubscription for Virtualized Clouds, Journal of Parallel and Distributed Computing, Vol. 74, Issue 4, pp. 2241-2256, April 2014, Sangwook Kim, Hwanju Kim, Joonwon Lee and Jinkyu Jeong
Analysis of Virtual Machine Live-Migration as a Method for Power-Capping, Journal of Supercomputing, Vol. 66, Issue 3, pp. 1629-1655, December 2013, Jinkyu Jeong, Sung-hun Kim, Hwanju Kim, Joonwon Lee, Euiseong Seo
Efficient Function Call Tracing with Link-Time Binary Rewriting for CE Devices, IEEE Transactions on Consumer Electronics, Vol. 59, Issue 4, pp. 892-900, November 2013, Bon-Keun Seo, Jinkyu Jeong, Joonwon Lee, and Euiseong Seo
Demand-Based Coordinated Scheduling for SMP VMs, ASPLOS 2013
-
- Data Intelligence and Learning (DIAL) Lab - Prof. JONGWUK LEE
-
- JONGWUK LEE
- https://diallab.github.io/
Major research interests
Recommender systems and informational retrieval
Natural language processing
Machine learning algorithms and optimization
Data mining and its real-world applications
Major research achievements (2021~present)
SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval, The 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022
Logit Mixing Training for More Reliable and Accurate Prediction, The 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022
FuseME: Distributed Matrix Computation Engine based on Cuboid-based Fused Operator and Plan Generation, ACM SIGMOD/PODS International Conference on Management on Data (SIGMOD), 2022
Bilateral Self-unbiased Learning from Biased Implicit Feedback, The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022
S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks, 15th ACM International Conference on Web Search and Data Mining (WSDM), 2022
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021
Railroad is Not a Train: Saliency as Pseudo-pixel Supervisions for Weakly Supervised Semantic Segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Session-aware Linear Item-Item Models for Session-based Recommendation, 30th The Web Conference (WWW), 2021
Local Collaborative Autoencoders, 14th ACM International Conference on Web Search and Data Mining (WSDM), 2021
-
- Software Engineering Lab - Prof. EUNSEOK LEE
-
Major research interests
Software Engineering & Automation in Testing & Debugging
Automatic SW Fault Localization
Machine learning-based automatic SW patch generation and program correction
Green SW creation and evaluation considering carbon emmisions
Major research achievements (2021~present)
"An Empirical Study of Deep Transfer Learning-based Program Repair for Kotlin Projects",European Software Engineering Conference and Symposium on the Foundations of Software Engineering(ESEC/FSE),pp.1441-1452, Nov.2022
"Deep Learning-based Production and Test Bug Report Classification using Source Files", International Conference on Software Engineering (ICSE), 2022 (short Accepted)
"How Does the First Buggy File Work Well for Iterative IR-based Bug Localization?", ACM/SIGAPP Symposium on Applied Computing (SAC), Apr. 2022
"Feature Assortment for Deep Learning-based Bug Localization With a Program Graph", ACM/SIGAPP Symposium on Applied Computing (SAC), Apr. 2022
"An Empirical Study of IR-based Bug Localization for Deep Learning-based Software", International Conference on Software Testing, Verification and Validation(ICST), Apr.2022
"Tracking Down Misguiding Terms for Locating Bugs in Deep Learning-based Software (short)", AAAI Conference on Artificial intelligence(AAAI), Feb.2022
"Multi-objective Optimization-based Bug-fixing Template Mining for Automated Program Repair", International Conference on Automated Software Engineering (ASE-NIER), Oct.2022
"Impact of Defect Instances for Successful Deep Learning-based Automatic Program Repair", International Conference on Software Maintenance and Evolution (ICSME-NIER), pp. 419-423, Oct.2022
"Are Datasets for Information Retrieval-based Bug Localization Techniques Trustworthy?", Empirical Software Engineering (ESE), Vol.26, No.3, pp.1-66, Mar.2021
"A Novel Automatic Query Expansion with Word Embedding for IR-based BugLocalization", International Symposium on Software Reliability Engineering (ISSRE), Oct.2021
-
- Computer Graphics Lab - Prof. SUNGKIL LEE
-
- SUNGKIL LEE
- http://cg.skku.edu/
Research interests
Real-time GPU rendering and Optics
Neural Rendering and View Synthesis
Synthetic data generation for deep learning
Display Algorithms for VR
GPU Algorithms
Major research achievements
Learning Camera Parameters with Weighted Edge Attention from Single-View Images. Moonsoo Jeong, Hyogeun Byun, and Sungkil Lee. IEEE Access, 11, 16896–16906, 2023.
Real-Time Dynamic Bokeh Rendering with Efficient Look-Up Table Sampling. Yuna Jeong, Seung Youp Baek, Yechan Seok, Gi Beom Lee, and Sungkil Lee. IEEE Trans. Visualization and Computer Graphics, 28(2), 1373–1384, 2022.
Hierarchical Raster Occlusion Culling. Gi Beom Lee, Moonsoo Jeong, Yechan Seok, and Sungkil Lee. Computer Graphics Forum (Proc. Eurographics'21), 40(2), 489–495, 2021. Presented at Eurographics 2021, Vienna, Austria (Virtual Conference)
Deep Defocus Map Estimation using Domain Adaptation. Junyong Lee, Sungkil Lee, Sunghyun Cho, and Seungyong Lee. IEEE Conf. Computer Vision and Patt. Recog. (CVPR), 12222–12230, 2019.
MegaViews: Scalable Many-View Rendering with Concurrent Scene-View Hierarchy Traversal. Timothy R. Kol, Pablo Bauszat, Sungkil Lee, and Elmar Eisemann. Computer Graphics Forum, 38(1), 235–247, 2019. Presented at Eurographics 2019, Genova, Italy.
Iterative Depth Warping. Sungkil Lee, Younguk Kim, and Elmar Eisemann. ACM Trans. Graphics, 37(5), 177:1–13, 2018. Presented at ACM SIGGRAPH 2019.
Automated Outdoor Depth-Map Generation and Alignment. Martin Cadik, Daniel Sykora, and Sungkil Lee. Elsevier Computers & Graphics, 74, 109–118, 2018.
Quad-Based Fourier Transform for Efficient Diffraction Synthesis. Leonardo Scandolo, Sungkil Lee, and Elmar Eisemann. Computer Graphics Forum (Proc. EGSR'18), 37(4), 167–176, 2018.
Efficient Ray Tracing Through Aspheric Lenses and Imperfect Bokeh Synthesis. Hyuntae Joo, Soonhyeon Kwon, Sangmin Lee, Elmar Eisemann, and Sungkil Lee. Computer Graphics Forum (Proc. EGSR'16), 35(4), 99–105, 2016.