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Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking (HT). How can we summarize them to convince law enforcement to act? Spotting micro-clusters of near-duplicate ...

Many assistive home robotics applications assume open-loop interactions: robots incorporate little feedback from people while autonomously completing tasks. This places undue burden on people to condition their actions and environment to maximize the ...

Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have ...

We introduce the concept of semantic fast-forwarding of video streams for efficient labeling of training data for activity recognition. We show that this concept can be realized by combining deep learning within individual frames, with spatial and ...

Within a large database 𝒢 containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs? Most existing work are designed for plain (unlabeled) and/or simple (unweighted) graphs. We introduce CODEtect, the first ...

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of ...

Occupant information inference with IoT sensor data enables many smart applications, such as patients'/older adults' in-home monitoring. The difficulty of collecting labeled real-world IoT sensor data often leads to reliability and scalability issues ...

With building heating, ventilation, and air conditioning (HVAC) systems accounting for 20% of the energy consumption in the United States, there is a need to develop and implement optimal control strategies for building HVAC systems that reduce energy ...

The rise of IoT devices brings a lot of security risks. To mitigate them, researchers have introduced various promising network-based anomaly detection algorithms, which oftentimes leverage machine learning. Unfortunately, though, their deployment and ...

Deep learning based speech enhancement in the short-time Fourier transform (STFT) domain typically uses a large window length such as 32 ms. A larger window can lead to higher frequency resolution and potentially better enhancement. This however incurs an ...

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their ...

We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically ...

Reconstructing and designing media with continuously-varying refractive index fields remains a challenging problem in computer graphics. A core difficulty in trying to tackle this inverse problem is that light travels inside such media along curves, ...

High-quality motion capture datasets are now publicly available, and researchers have used them to create kinematics-based controllers that can generate plausible and diverse human motions without conditioning on specific goals (i.e., a task-agnostic ...

Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However, the current machine learning approaches miss a key element of the ...

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Systems for training massive deep learning models (billions of parameters) today assume and require specialized "hyperclusters": hundreds or thousands of GPUs wired with specialized high-bandwidth interconnects such as NV-Link and Infiniband. Besides ...

This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization, EEND methods are better in terms of speaker overlap handling. However, EEND ...

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, ...