Machine learning in/with information fusion for infrastructure understanding, panel summary

During the 2019 SPIE DSS conference, panelists were invited to highlight the trends and use of artificial intelligence and machine learning (AI/ML) for information fusion. The common themes between the panelists include leveraging AI/ML coordinated with Information Fusion for: (1) knowledge reasoning, (2) model building, (3) object recognition and tracking, (4) multimodal learning, and (5) information processing. The opportunity for machine learning exists within all the fusion levels of the Data Fusion Information Group model.

[1]  Herbert Edelsbrunner,et al.  Computational Topology - an Introduction , 2009 .

[2]  Genshe Chen,et al.  Context aided video-to-text information fusion , 2014, 17th International Conference on Information Fusion (FUSION).

[3]  Hai Xuan Pham,et al.  Robust real-time performance-driven 3D face tracking , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[4]  E. Blasch,et al.  Sensor Management Fusion Using Operating Conditions , 2008, 2008 IEEE National Aerospace and Electronics Conference.

[5]  Lynne L. Grewe,et al.  ULearn: understanding and reacting to student frustration using deep learning, mobile vision and NLP , 2019, Defense + Commercial Sensing.

[6]  Stelios C. A. Thomopoulos,et al.  Panel summary of cyber-physical systems (CPS) and Internet of Things (IoT) opportunities with information fusion , 2017, Defense + Security.

[7]  Jing Peng,et al.  Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Erik Blasch,et al.  Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge , 2016 .

[9]  Roozbeh Jafari,et al.  Multi-sensor data-driven: synchronization using wearable sensors , 2015, SEMWEB.

[10]  Eloi Bosse,et al.  High-Level Information Fusion Management and System Design , 2012 .

[11]  Rabab Kreidieh Ward,et al.  Deep learning for pixel-level image fusion: Recent advances and future prospects , 2018, Inf. Fusion.

[12]  Jose A. Perea,et al.  Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis , 2013, Found. Comput. Math..

[13]  Erik Blasch,et al.  Issues and challenges in resource management and its interaction with levels 2/3 fusion with applications to real-world problems: an annotated perspective , 2007, SPIE Defense + Commercial Sensing.

[14]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[15]  Erik Blasch,et al.  Multi-Intelligence Analytics for Next Generation Analysts (MIAGA) , 2016, SPIE Defense + Security.

[16]  Ivan Kadar,et al.  Adaptive MaxEnt modeling of distributed decision fusion without knowledge of prior probabilities of local decisions , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[17]  Zheng Liu,et al.  Multispectral Image Fusion and Colorization , 2018 .

[18]  Genshe Chen,et al.  Analysis and visualization of large complex attack graphs for networks security , 2007, SPIE Defense + Commercial Sensing.

[19]  Erik Blasch,et al.  ESCAPE Data Collection for Multi-Modal Data Fusion Research , 2019, 2019 IEEE Aerospace Conference.

[20]  Ivan Kadar,et al.  A framework for adaptive MaxEnt modeling within distributed sensors and decision fusion for robust target detection/recognition , 2018, Defense + Security.

[21]  Christopher Joseph Pal,et al.  Describing Videos by Exploiting Temporal Structure , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[23]  Maja Pantic,et al.  Social Signal Processing , 2017 .

[24]  Adam Watkins,et al.  Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers , 2015, Defense + Security Symposium.

[25]  Ronghua Xu,et al.  Real-Time Human Objects Tracking for Smart Surveillance at the Edge , 2018, 2018 IEEE International Conference on Communications (ICC).

[26]  Liang Zhao,et al.  The EMBERS architecture for streaming predictive analytics , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[27]  John J. Salerno,et al.  Summary of human social, cultural, behavioral (HSCB) modeling for information fusion panel discussion , 2013, Defense, Security, and Sensing.

[28]  Rainer Stiefelhagen,et al.  Aligning plot synopses to videos for story-based retrieval , 2015, International Journal of Multimedia Information Retrieval.

[29]  Di Qiu,et al.  Ground target track bias estimation using opportunistic road information , 2010, Proceedings of the IEEE 2010 National Aerospace & Electronics Conference.

[30]  Chee-Yee Chong,et al.  Graph approaches for data association , 2012, 2012 15th International Conference on Information Fusion.

[31]  Genshe Chen,et al.  BlendCAC: A BLockchain-Enabled Decentralized Capability-Based Access Control for IoTs , 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[32]  James Llinas,et al.  High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges , 2012, IEEE Aerospace and Electronic Systems Magazine.

[33]  Jose B. Cruz,et al.  Game Theoretic Approach to Threat Prediction and Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[34]  Alan L. Yuille,et al.  Generation and Comprehension of Unambiguous Object Descriptions , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Erik Blasch,et al.  Issues and Challenges in Situation Assessment (Level 2 Fusion) , 2006, J. Adv. Inf. Fusion.

[36]  Genshe Chen,et al.  A federated capability-based access control mechanism for internet of things (IoTs) , 2018, Defense + Security.

[37]  Lynne L. Grewe,et al.  Drone based user and heading detection using deep learning and stereo vision , 2019, Defense + Commercial Sensing.

[38]  Vladimir Pavlovic,et al.  Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach , 2018, IEEE Transactions on Image Processing.

[39]  Erik Blasch,et al.  Situation, impact, and user refinement , 2003, SPIE Defense + Commercial Sensing.

[40]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[41]  Genshe Chen,et al.  Space object classification using deep neural networks , 2018, 2018 IEEE Aerospace Conference.

[42]  Erik Blasch,et al.  Revisiting the JDL model for information exploitation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[43]  Ivan Kadar,et al.  Deep learning in AI and information fusion panel discussion , 2019, Defense + Commercial Sensing.

[44]  Erik Blasch,et al.  Covert photo classification by deep convolutional neural networks , 2017, Machine Vision and Applications.

[45]  Erik Blasch,et al.  One decade of the Data Fusion Information Group (DFIG) model , 2015, Commercial + Scientific Sensing and Imaging.

[46]  Erik Blasch,et al.  Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition , 2013, Defense, Security, and Sensing.

[47]  Erik Blasch,et al.  Enhanced air operations using JView for an air-ground fused situation awareness udop , 2013, 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC).

[48]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[49]  Louis-Philippe Morency,et al.  Multimodal Machine Learning: A Survey and Taxonomy , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Erik Blasch,et al.  Comparison of information theoretic divergences for sensor management , 2011, Defense + Commercial Sensing.

[51]  E. L. Waltz.  Information understanding: integrating data fusion and data mining processes , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[52]  Brian Hutchinson,et al.  Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams , 2017, AAAI Workshops.

[53]  Erik Blasch,et al.  Mobile positioning via fusion of mixed signals of opportunity , 2014, IEEE Aerospace and Electronic Systems Magazine.

[54]  Yuxin Peng,et al.  CM-GANs , 2019, ACM Trans. Multim. Comput. Commun. Appl..

[55]  Erik Blasch,et al.  QuEST for Information Fusion in Multimedia Reports , 2014, Int. J. Monit. Surveillance Technol. Res..

[56]  Genshe Chen,et al.  Manifold learning algorithms for sensor fusion of image and radio-frequency data , 2018, 2018 IEEE Aerospace Conference.

[57]  Erik Blasch,et al.  Methods of AI for Multimodal Sensing and Action for Complex Situations , 2019, AI Mag..

[58]  Erik Blasch,et al.  BlendMAS: A Blockchain-Enabled Decentralized Microservices Architecture for Smart Public Safety , 2019, 2019 IEEE International Conference on Blockchain (Blockchain).

[59]  Gunnar E. Carlsson,et al.  Topology and data , 2009 .

[60]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[61]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[62]  Adam Watkins,et al.  Topological and statistical behavior classifiers for tracking applications , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[63]  Erik Blasch,et al.  DDDAS ADVANTAGES FROM HIGH-DIMENSIONAL SIMULATION , 2018, 2018 Winter Simulation Conference (WSC).

[64]  Erik Blasch,et al.  Random-point-based filters: analysis and comparison in target tracking , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[65]  David Cohen-Steiner,et al.  Stability of Persistence Diagrams , 2005, Discret. Comput. Geom..

[66]  Gunnar E. Carlsson,et al.  A look at the topology of convolutional neural networks , 2018, ArXiv.