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The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user ...

The ever-increasing demand for energy is resulting in considerable carbon emissions from the electricity grid. In recent years, there has been growing attention on demand-side optimizations to reduce carbon emissions from electricity usage. A vital ...

Access to electricity is crucial for poverty reduction and economic growth. However, almost 759 million people still do not have access to electricity. More than 90% are located in the global South, where low-income countries struggle to provide clean, ...

Non-intrusive load monitoring (NILM) refers to the task of disaggregating total household power consumption into the constituent appliances. In recent years, various neural network (NN) based approaches have emerged as state-of-the-art for NILM. In ...

Machine learning-based predictions are popular in many applications including healthcare, recommender systems and finance. More recently, the development of low-end edge hardware (e.g., Apple’s Neural Engine and Intel’s Movidius VPU) has provided a ...

We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map. Our architecture is based on a transformer with a novel attention mechanism. Our key idea is ...

Proper indoor ventilation through buildings' heating, ventilation, and air conditioning (HVAC) systems has become an increasing public health concern that significantly impacts individuals' health and safety at home, work, and school. While much work ...

The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 ...

The Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) is India's premier conference in Computer Vision, Graphics, Image Processing and related fields. Started in 1998, it is a biennial international conference providing a ...

Artificial Intelligence is becoming ubiquitous in products and services that we use daily. Although the domain of AI has seen substantial improvements over recent years, its effectiveness is limited by the capabilities of current computing technology. ...

Machine learning algorithms in the field of economics and game theory usually involve computing an intermediate valuation function from data samples and using this approximate function to compute desired solution concepts. This approach has several ...

Agents operating in the open world often produce negative side effects (NSE), which are difficult to identify at design time. We examine how a human can assist an agent, beyond providing feedback, and exploit their broader scope of knowledge to mitigate ...

Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance over ...

We present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first ...

We consider deploying an object detection pipeline over a heterogeneous IoT network. We consider a setting where a camera-equipped IoT edge node communicates wirelessly with a cloud server. In many real-world domains, the bandwidth of this connection is ...

In this article, we introduce and analyze an extension to the matching problem on a weighted bipartite graph (i.e., the assignment problem): Assignment with Type Constraints. Here, the two parts of the graph are each partitioned into subsets, called ...

Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose ...

We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint ...

Motif identification has been one of the most widely studied problems in bioinformatics. Many methods have been developed to discover binding motifs from a large set of genes. But when the given genes are only a partial set of target genes, the ...

Recent years have seen a push towards deploying fully autonomous robots in large, complex domains such as autonomous driving, space exploration, and service robots. However, legal, ethical, or technical constraints have limited the extent of these ...