Selected Publications

Il-Chul Moon and Kathleen M. Carley, Modeling and Simulation of Terrorist Networks in Social and Geospatial dimensions, IEEE Intelligent Systems, Special Issue on Social Computing, Vol. 22, pp 40-49, Sep/Oct. 2007

2011.06.20 14:10

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Il-Chul Moon and Kathleen M. Carley, Modeling and Simulation of Terrorist Networks in Social and Geospatial dimensions, IEEE Intelligent Systems, Special Issue on Social Computing, Vol. 22, pp 40-49, Sep/Oct. 2007

 

Abstract : 

 A simple theoretical multiagent model reasons about the criticality of terrorists and regions as terrorist interactions coevolve in geographical and social spaces. Social and spatial relations evolve over time. Estimating their evolutions is important for management, command and control structures, and intelligence analysis research. By knowing future agent social and spatial distributions, an analyst can identify emergent leaders, hot spots, and organizational vulnerabilities. Historically, such estimations have depended heavily on qualitative data analyses by subject-matter experts. A few researchers approached the issue using multiagent models and simulation. The models addressed the complex nature of the organization and task assignments, resource distributions, or agent locations. The simulations addressed the near-term organizational changes. This research came from two perspectives: the effects of change in the social network and the effects of geospatial change. Both perspectives can project aspects of emerging organizational structure and future performance, but they can't examine the interaction between physical and social movements.

 

@ARTICLE{4338493, 
author={I. C. Moon and K. M. Carley}, 
journal={IEEE Intelligent Systems}, 
title={Modeling and Simulating Terrorist Networks in Social and Geospatial Dimensions}, 
year={2007}, 
volume={22}, 
number={5}, 
pages={40-49}, 
keywords={multi-agent systems;social aspects of automation;terrorism;geospatial change;geospatial dimensions;organizational changes;social dimensions;social network;terrorist interactions;terrorist networks;theoretical multiagent model;Analytical models;Command and control systems;Data analysis;Data mining;Data visualization;Intelligent control;Intelligent structures;Moon;Resource management;Text analysis;coherence and coordination;distributed artificial intelligence;multiagent systems;social networks}, 
doi={10.1109/MIS.2007.4338493}, 
ISSN={1541-1672}, 
month={Sept},}

 

Source Website : 

 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4338493

 

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36 DongHyeok Shin, Seungjae Shin, Il-Chul Moon, Frequency Domain-based Dataset Distillation, Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA, Dec 10-Dec 16, 2023
35 Suhyeon Jo, Donghyeok Shin, Byeonghu Na, JoonHo Jang, and Il-Chul Moon, Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy, ACM International Conference on Information and Knowledge Management (CIKM 2023) file
34 Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, SAAL: Sharpness-Aware Active Learning, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023 file
33 Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon, Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023 file
32 Seungjae Shin, Heesun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon, Loss Curvature Matching for Dataset Selection and Condensation, International Conference on Artificial Intelligence and Statistics (AISTATS 23), Valencia, Spain, Apr 25-27, 2023 file
31 Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon, Maximum Likelihood Training of Implicit Nonlinear Diffusion Model, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022 file
30 JoonHo Jang, Byeonghu Na, Dong Hyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon, Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022 file
29 Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation, International Conference on Machine Learning (ICML 2022) file
28 HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon, From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model, International Conference on Machine Learning (ICML 2022) file
27 Yooon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon, LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning, Neural Information Processing Systems (NeurIPS 2021), Virtual, Dec. 7-10, 2021 file
26 Kim, D., Yun, TS., Moon, IC. et al. Automatic calibration of dynamic and heterogeneous parameters in agent-based models. Auton Agent Multi-Agent Syst 35, 46 (2021). file
25 Mingi Ji, Seungjae Shin, Seunghyun Hwang, Gibeom Park, Il-Chul Moon. Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation. Conference on Computer Vision and Pattern Recognition (CVPR 2021). file
24 Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song, Weonyoung Joo, Wanmo Kang, and Il-Chul Moon, Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder, AAAI Conference on Artificial Intelligence (AAAI 2021) file
23 Kyungwoo Song, Yohan Jung, Dongjun Kim, and Il-Chul Moon, Implicit Kernel Attention, AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, Feb. 2-9 file
22 Byeonghu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoon-Yeong Kim, and Il-Chul Moon, Deep Generative Positive-Unlabeled Learning under Selection Bias, ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, Oct. 19 file
21 Joo, W., Lee, W., Park, S., & Moon, I. C. (2020). Dirichlet variational autoencoder. Pattern Recognition, Vol. 107, 107514. file
20 Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, and Il-Chul Moon, Sequential Recommendation with Relation-Aware Kernelized Self-Attention​. AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
19 Su-Jin Shin, Kyungwoo Song, and Il-Chul Moon, Hierarchically Clustered Representation Learning, AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
18 Kyungwoo Song, JoonHo Jang, Seung jae Shin and Il-Chul Moon, Bivariate Beta-LSTM. AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
17 Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, and Il-Chul Moon, Adversarial Dropout for Recurrent Neural Networks. AAAI Conference on Artificial Intelligence (AAAI 2019). Hawaii. Jan. 27-Feb. 1 file
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