Nowadays, crowdsourcing has become an increasingly popular paradigm for large-scale data collection, annotation, and classification. Today’s rapid growth of crowdsourcing platforms calls for effective worker selection mechanisms, which oftentimes have to operate with a priori unknown worker reliability. We discover that the empirical entropy of workers’ results, which measures the uncertainty in the final aggregated results, naturally becomes a suitable metric to evaluate the outcome of crowdsourcing tasks. Therefore, this paper designs a worker selection mechanism that minimizes the empirical entropy of the results submitted by participating workers. Specifically, we formulate worker selection under sequentially arriving tasks as a combinatorial multi-armed bandit problem, which treats each worker as an arm, and aims at learning the best combination of arms that minimize the cumulative empirical entropy. By information theoretic methods, we carefully derive an estimation of the upper confidence bound for empirical entropy minimization, and leverage it in our minimum entropy upper confidence bound (ME-UCB) algorithm to balance exploration and exploitation. Theoretically, we prove that ME-UCB has a regret upper bound of O(1), which surpasses existing submodular UCB algorithms. Our extensive experiments with both a synthetic and real-world dataset empirically demonstrate that our ME-UCB algorithm outperforms other state-of-the-art approaches.

Recent years have witnessed the increasing popularity of electric buses (e-buses) around the globe due to their environment friendly nature. However, various factors, such as the prohibitive purchasing costs and the scarcity of large-scale charging facilities, hinder the wider adoption of e-buses. Thus, to effectively cut the cost of building and maintaining urban e-bus systems, we optimize the dispatching strategy for urban e-bus systems to satisfy public transportation demands with the minimum e-bus fleet. Specifically, we propose to systematically exploit at city-scale cross-line dispatching, a smart dispatching strategy allowing one bus to serve multiple bus lines when necessary. Technically, we construct a novel and generalizable graph-theoretic model for urban e-bus systems integrating e-buses non-negligible charging time, the spatio-temporal constraints of bus trips, and various other real-world factors. We prove that it is NP-hard, and has no $(2-\epsilon)$ -approximation algorithm. Next, we propose a polynomial-time algorithm solving the problem with a guaranteed approximation ratio. Furthermore, we conduct extensive experiments on a large-scale real-world bus dataset from Shenzhen, China, which validate the effectiveness of our algorithms. As shown by our experimental results, to serve 300 bus lines, our dispatching strategy needs 38.2\% less e-buses than the one currently used in practice.

Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize the dental condition of each tooth. We further develop a data-driven signal processing pipeline to detect and discriminate different dental conditions. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts further validate ToMoBrush's potential in enhancing at-home dental healthcare.

Soft and miniaturized robots possess the capability to operate inside narrow, confined environments. However, powering soft robots inside these environments with on-board batteries or wired connections to external power supplies can significantly restrain their mobility. Similarly, wireless actuation approaches are constrained by near-field actuation, line-of-sight operation, or indiscriminate actuation of many actuators. To provide higher mobility for wireless soft robot to operate inside non-line-of-sight scenarios, we present a radio-frequency system that introduces frequency-selective actuation of liquid crystal elastomer actuators. We create liquid crystalline elastomer actuators with a low actuation temperature and embed them with conductive traces that resonate and heat by selected frequencies of radio-frequency excitation in the 2.40 GHz range. We further develop a wireless actuation platform that infers the wireless channel and beamforms towards the actuator to achieve efficient beamforming. Demonstrations show our system is capable of selectively actuating different actuators while the robot is in motion and obstructed by occlusions.

This paper proposes a new primitive that allows soft robots to be physically controlled in a completely non-line-of-sight context using wireless energy - a process we call wireless actuation. Soft robots, which are composed entirely of soft materials and exclude any rigid components, are highly flexible platforms that can change their shape. This paper considers a specific class of soft robots composed of liquid-crystal elastomers (LCE) that are entirely electronics-free and engineered to change shape when heated to 60 °C. Traditionally, such robotic systems must be in line-of-sight of a light source, such as infrared to be moved, or require an external power supply for Joule heating and often take several tens of seconds to heat. We present WASER, a novel RF-based heating platform that allows electronics-free robots to be actuated rapidly (within a few seconds) and potentially in non-line-of-sight. WASER achieves this through innovations in both wireless systems and material science. On the wireless front, WASER develops a new blind beamforming solution that directs high-power wireless energy at fine spatial granularity without electronics on the robot to provide feedback. On the material science front, WASER exhibits heat-responsive shape-morphing and energy-harvesting material functionalities that allow for rapid wireless heating. We implement and evaluate WASER and demonstrate diverse shape-morphing capabilities.

Microwave ovens have become an essential cooking appliance owing to their convenience and efficiency. However, microwave ovens suffer from uneven distribution of energy, which causes prolonged delays, unpleasant cooking experiences, and even safety concerns. Despite significant research efforts, current solutions remain inadequate. In this paper, we first conduct measurement studies to understand the energy distribution for 10 microwave ovens and show their energy distribution in both 2D and 3D is very skewed, with notably lower energy levels at the center of the microwave cavity, where food is commonly placed. To tackle this challenge, we propose a novel methodology to enhance the performance of microwave ovens. Our approach begins with the development of a measurement driven model of a microwave oven. We construct a detailed 3D model in the High Frequency Structure Simulator (HFSS) and use real temperature measurements from a microwave to derive critical parameters relevant to the appliance's functionality (e.g., operating frequency, waveguide specifications). We then develop a novel approach that optimizes the design and placement of a low-cost passive metasurface for a given heating objective. Using extensive experiments, we demonstrate the efficacy of our approach across diverse food, optimization objectives, and microwave ovens.