Video Description Generator [VDG]
We design a video-to-language model structure to generate natural language description (in English) of video clips automatically. The techniques involved are CNN, LSTM and attention model.
This project uses data from the Microsoft Multimedia Challenge (MSR-VTT).
We use deep learning based approach (specifically Recurrent and Convolutional Neural Network) to model workflow data in real-life, and understand how activities influence the future activities and other attributes.
We use sensor data and machine learning algorithm to recognize real-life activities, and then perform classification and prediction on other attributes of the activities. E.g., the activities' execution forms the process, and we would like to know what is the current progress of process from the activities. We proposed a new system of Process Progress Estimation and Phase Detection to detect current phase and progress of the whole process. The estimation can predict the time left for the process based on observed activities and statistical distribution of activities/phases duration.
In this project, we analyze the workflow both by data-driven techniques and by comparison to the expert model.
Data-driven techniques: We use trace alignment method to align activities in workflow records so that common patterns of activities are captured. The common patterns are represented by the consensus sequence in the alignment. The higher frequency of an activity in one column indicates the activity is more likely to happen in such place. Though trace alignment deals with sequential data, the original algorithm does not support the attribute of duration. We proposed a Duration-Aware Alignment of Process Traces method to handle real-life workflow data with duration. To evaluate the alignment algorithm's performance, several metrics have been proposed, a summary of these metrics and some modifications are in our Trace Alignment Evaluation Methods.
Expert model techniques: The consistency of a workflow case and the expert model can be measured by conformance checking, which compares workflow data to a graph-based expert model and calculates the difference. The less difference indicates workflow data matches expert model more, vice versa. Though the expert model is predefined based on domain knowledge, which may be subjective and biased, the expert model based methods are necessary to guide other data-driven methods with its human labels.
Built modeling system based on Generative Adversarial Network and Reinforcement Learning methods, which models the adversarial procedure in game and calculates real-time prediction of the wining probability of both sides.
The API request and database maintenance script is here.
Use machine learning and other statistical methods to analyze stock prices and bioinformatics data. The algorithms are developed based on Weka library in Java, and the software GUI is built on Java.Swingx. The bioinformatics data is usually high-dimensional and imbalance, I use certain dimension reduction methods to project the features to lower dimension (left figure), and perform clustering/classification on these low-dimensional features.
I also developed auto sampling method on sensor data with statistical range from historical sample data, and integrate it with the software diagnostic platform, which replaced much of human sampling workload.