Interacting with not only virtual but also real objects, or even virtual objects augmented by real objects becomes a trend of virtual reality (VR) interactions and is common in augmented reality (AR). However, current haptic shape rendering devices generally focus on feedback of virtual objects, and require the users to put down or take off those devices to perceive real objects. Therefore, we propose FingerX to render haptic shapes and enable users to touch, grasp and interact with virtual and real objects simultaneously. An extender on the fingertip extends to a corresponding height to support between the fingertip and the real objects or the hand, to render virtual shapes. A ring rotates and withdraws the extender behind the fingertip when touching real objects. By independently controlling four extenders and rings on each finger with the exception of the pinky finger, FingerX renders feedback in three common scenarios, including touching virtual objects augmented by real environments (e.g., a desk), grasping virtual objects augmented by real objects (e.g., a bottle) and grasping virtual objects in the hand. We conducted a shape recognition study to evaluate the recognition rates for these three scenarios and obtained an average recognition rate of 76.59% with shape visual feedback. We then performed a VR study to observe how users interact with virtual and real objects simultaneously and verify that FingerX significantly enhances VR realism, compared to current vibrotactile methods.

Building contextual models for new “smart” environments is not considered cost effective if data for model training must be collected from scratch. It is more practical to transfer as much learned knowledge as possible from an existing environment to the new target environment in order to reduce the data collection effort. In order to reuse learned knowledge from an original environment, this study proposed a feature-based knowledge transfer framework. The framework makes use of transfer learning, which relaxes the constraint requiring model training and testing datasets to be highly similar in distribution. Experimental results show that this framework can successfully help extract and transfer knowledge between two different smart-home environments. Models trained via the proposed framework can even outperform nontransfer-learning models by up to 8% in accuracy. Finally, the flexibility of the proposed framework enables used as a test bed for evaluating different methods and models in order to improve the service quality of human-centric context-aware applications.

Recommender systems are useful tools that help people to filter and explore massive information. While the accuracy of recommender systems is important, many recent research indicated that focusing merely on accuracy not only is insufficient to meet user needs, but also may be harmful. Other characteristics such as novelty, unexpectedness and diversity should also be taken into consideration. Previous work has shown that more the sales of long-tail items could be more beneficial to both customers and some business models. However, the majority of collaborative filtering approaches tends to recommend popular selling items.

Activity recognition (AR) is a key enabler for a context-aware smart home since knowing what the residents’ current activities helps a smart home provide more desirable services. This is why AR is often used in assistive technologies for cognitively impaired people to evaluate their abilities to undertake activities of daily living. In a real-life scenario, multiple-resident AR has been considered as a very challenging problem, primarily due to the complexity of data association. In addition, most prior research has not considered the potential interpersonal interactions among residents to simplify complexity, especially in an environment monitored by ambient sensors. In this study, we propose two types of multiuser activity models, both of which are derived from an interaction-feature enhanced multiuser model learning framework. These two models consider interpersonal interactions and data association for multiuser AR using ambient sensors. We then compare their performance with the other two baseline models with or without consideration of data association and interpersonal interactions. The experimental results show that the derived models outperform other baseline classifiers. Therefore, the proposed approach can increase the opportunities for providing context-aware services for a multiresident smart home.

A large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows participants to cooperate on various computations while retaining the privacy of their own input data, which is an ideal solution to this issue. Although there is a number of frameworks developed in SMC to meet this challenge, but they are either tailored to perform only on specific tasks or provide very limited precision. In this paper, we have developed protocols for floating point arithmetic based on secure scalar product protocols, which is re- quired in many real world applications. Our protocols follow most of the IEEE-754 standard, supporting the four fundamental arithmetic operations, namely addition, subtraction, multiplication, and division. We will demonstrate the practicality of these protocols through performing various statistical calculations that is widely used in most data analysis tasks. Our experiments show the performance of our framework is both practical and promising.

Multi-resident activity recognition is among a key enabler in many context-aware applications in a smart home. However, most of prior researches ignore the potential interactions among residents in order to simplify problem complexity. On the other hand, multiple-resident activities are usually recognized using cameras or wearable sensors. However, due to human-centric concerns, it is more preferable to avoid using obtrusive sensors. In this paper, we propose dynamic Bayesian networks which extend coupled hidden Markov models (CHMMs) by adding some vertices to model both individual and cooperative activities. In order to improve performance of the model, we categorize sensor observations based on data association and some domain knowledge to model multiple-resident activity patterns. We then validate the performance using a multi-resident dataset from WSU (Washington State University), which only includes non-obtrusive sensors. The experimental result shows that our model performs better than other baseline classifiers.

We introduce and study orderly spanning trees of plane graphs. This algorithmic tool generalizes canonical orderings, which exist only for triconnected plane graphs. Although not every plane graph admits an orderly spanning tree, we provide an algorithm to compute an orderly pair for any connected planar graph G, consisting of an embedded planar graph H isomorphic to G, and an orderly spanning tree of H. We also present several applications of orderly spanning trees: (1) a new constructive proof for Schnyder's realizer theorem, (2) the first algorithm for computing an area-optimal 2-visibility drawing of a planar graph, and (3) the most compact known encoding of a planar graph with O(1)-time query support. All algorithms in this paper run in linear time.