Paper accepted in CD-MAKE 2020
Amin Anjomshoaa, Edward Curry, “Inter-space Machine Learning in Smart Environments“, International Cross-Domain Conference for Machine Learning and Knowledge Extraction, pp. 535-549. Springer, Cham, 2020.
Paper accepted in GIScience 2020
Piyush Yadav, Dipto Sarkar, Dhaval Salwala, Edward Curry, “Human Assisted Artificial Intelligence Based Technique to Create Natural Features for OpenStreetMap“, International Conference on Geographic Information Science (GIScience) 2020.
Paper accepted in ICMR 2020
Asra Aslam and Edward Curry. “Reducing Response Time for Multimedia Event Processing using Domain Adaptation.” Proceedings of the 2020 ACM on International Conference on Multimedia Retrieval (ICMR).
Paper accepted in ICMR 2020
Asra Aslam and Edward Curry, “Object Detection for Unseen Domains while Reducing Response Time using Knowledge Transfer in Multimedia Event Processing.” Proceedings of the 2020 ACM on International Conference on Multimedia Retrieval (ICMR).
Paper published in IJGC
Abstract: Complex Event Processing (CEP) systems is an event pro- cessing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, the CEP system is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform match- ing over them.…
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Paper presented in ICMLA 2019
Abstract: Modelling complex events in unstructured data like videos not only requires detecting objects but also the spatiotemporal relationships among objects. Complex Event Processing (CEP) systems discretize continuous streams into fixed batches using windows and apply operators over these batches to detect patterns in real-time. To this end, we apply CEP techniques over video streams…
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Paper presented in Middleware Doctoral Symposium 2019
Abstract: Complex Event Processing (CEP) is an event processing paradigm capable of detecting patterns over streaming data in real-time. Presently, CEP systems have key challenges to preform matching over video streams due to their unstructured data model and complex video patterns which occurs over time and space. In this paper, I introduce the design, implementation…
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Paper presented in Middleware 2019
This work presents a data-driven adaptive windowing approach to accelerate video content extraction in DNN-based Complex Event Processing (CEP) systems. The CEP windows continuously monitor low-level content of incoming video frames and exploit interframe correlations to accelerate the overall DNN content extraction process. The two main contributions are: 1) technique to create micro-batches of similar…
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Paper presented in ISM 2019
Machine learning based applications that run on image datasets increasingly use local image feature descriptors. We can visualize images as objects and local features as parts. Typically, there are thousands of local features per image, resulting in an explosion of feature set size for already huge image datasets. In this paper, we present a feature…
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Paper presented in BigData 2019
Video data is highly expressive and has traditionally been very difficult for a machine to interpret. Querying event patterns from video streams is challenging due to its unstructured representation. Middleware systems such as Complex Event Processing (CEP) mine patterns from data streams and send notifications to users in a timely fashion. Current CEP systems have…
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