Motion capture data is a digital representation of the complex temporal structure of human motion. Motion capture is widely used for data-driven animation in sports,medicine and entertainment, because of its ability to capture complex and realistic
motions. Due to its efficiency and cost, methods for reusing collections of motion capture...
Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature relevance feedback has the potential to dramatically increase classification performance. We review previous work on incorporating feature relevance feedback in the form of labeled features...
Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block, DNA. With this transformation's potential a host of brand-new challenges have emerged, many of which lend themselves to being solved through computational methods. From de...
The study of physical activity is important in improving people’s health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has...
Physical activity recognition using accelerometer data is a rapidly emerging field with many real-world applications. Much of the previous work in this area has assumed that the accelerometer data has already been segmented into pure activities, and the activity recognition task has been to classify these segments. In reality, activity...
Image classification is a difficult problem, often requiring large training sets to get satisfactory results. However this is a task that humans perform very well, and incorporating user feedback into these learning algorithms could help reduce the dependency on large amounts of labeled training data. This process has already been...
Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute...
One of the tasks that continues to prove difficult in robotics is the ability to grasp objects of varying shapes. It is time-consuming to acquire large amounts of real-world data in order to train accurate classifiers that can predict the success or failure of a grasp. To solve this issue,...
The computational identification of gene Transcription Start Sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are...