Open-domain personalized dialog system using user-interested topics in system responses

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

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Open Domain New Word Detection Using Condition Random Field Method

Journal of Software ◽

10.3724/sp.j.1001.2013.04254 ◽

2013 ◽

Vol 24 (5) ◽

pp. 1051-1060 ◽

Cited By ~ 7

Author(s):

Fei CHEN ◽

Yi-Qun LIU ◽

Chao WEI ◽

Yun-Liang ZHANG ◽

Min ZHANG ◽

...

Keyword(s):

Random Field ◽

Field Method ◽

Open Domain ◽

Word Detection

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End-to-End Task-Oriented Dialog System Through Template Slot Value Generation

10.21437/interspeech.2020-2011 ◽

2020 ◽

Author(s):

Teakgyu Hong ◽

Oh-Woog Kwon ◽

Young-Kil Kim

Keyword(s):

Dialog System ◽

End To End ◽

Task Oriented

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Coarse-To-Careful: Seeking Semantic-Related Knowledge for Open-Domain Commonsense Question Answering

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9413878 ◽

2021 ◽

Author(s):

Luxi Xing ◽

Yue Hu ◽

Jing Yu ◽

Yuqiang Xie ◽

Wei Peng

Keyword(s):

Question Answering ◽

Open Domain

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Natural convection of Al2O3-water nanosuspension in a semi-open domain with composite fin

Physics of Fluids ◽

10.1063/5.0042628 ◽

2021 ◽

Vol 33 (3) ◽

pp. 033606

Author(s):

Elena V. Shulepova ◽

Mikhail A. Sheremet ◽

Hakan F. Oztop

Keyword(s):

Natural Convection ◽

Open Domain

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Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽

10.3390/drones5030066 ◽

2021 ◽

Vol 5 (3) ◽

pp. 66

Author(s):

Rahee Walambe ◽

Aboli Marathe ◽

Ketan Kotecha

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Data Augmentation ◽

Test Time ◽

Complex Task ◽

Open Domain ◽

End User ◽

Aerial Vehicle ◽

Uav Images ◽

Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

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