\textit{SummerTime} seeks to summarize globally time series signals and provides a fixed-length, robust summarization of the variable-length time series. Many classical machine learning methods for cl...
With the increase of elderly population, Alzheimer's Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to rece...
With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant data in a way that captures the general state of the vehicle in a com...
We propose a novel methodology to define, analyze and forecast market states. In our approach, market states are identified by a reference sparse precision matrix and a vector of expectation values. I...
We propose a novel methodology based on subspace clustering for detecting, modeling and interpreting aquatic drone states in the context of autonomous water monitoring. It enables both more informativ...
We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an a...
Twitter hashtags provide a high-level summary of tweets, while cluster hashtags have many applications. Existing text-based methods (relying on explicit words in tweets) are greatly affected by the sp...
Timely recognition of driving intention is crucial in the design of a safe and effective driving assistance system. This study proposes an efficient recognition approach based on Nonlinear Polynomial ...
Time series modeling aims to capture the intrinsic factors underpinning observed data and its evolution. However, most existing studies ignore the evolutionary relations among these factors, which are...
This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining s...
The ubiquity and remarkable technological progress of wearable consumer devices and mobile-computing platforms (smart phone, smart watch, tablet), along with the multitude of sensor modalities availab...
The similarity measurement of time series is a significant approach to mine the rich and valuable law information hidden in the massive time series data. As the most advantageous approach in measuring...
The high volatility of world soybean prices has caused uncertainty and vulnerability particularly in the developing countries. The clustering of time series is a serviceable tool for discovering soybe...
The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Given the enorm...
The controlled experiment is an important scientific method for researchers seeking to determine the influence of the intervention, by interpreting the contrast patterns between the temporal observati...
The automatic recognition of different riding patterns in the context of naturalistic riding studies (NRSs) facilitates the behavioral analysis of powered two-wheelers (PTW), which is a challenging pr...
The accurate and interpretable prediction of future events in time-series data often requires the capturing of representative patterns (or referred to as states) underpinning the observed data. To thi...
The accurate and interpretable prediction of future events in time-series data often requires the capturing of representative patterns (or referred to as states) underpinning the observed data. To thi...
Speaker diarization is the task of determining "who speaks when" in an audio stream that usually contains an unknown amount of speech from an unknown number of speakers. Speaker diarization systems ar...
Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from diffe...