Bidirectional User Research Articles

CTITF: A tensor factorization model with constrained bidirectional user trust and implicit feedback for context-aware recommender systems

Recommender systems offer an efficient solution to the problem of information overload, which is exacerbated by the rapid expansion of data. Context-aware recommender systems (CARS) have become prominent, incorporating contextual information to provide more desirable recommendations. While the dimension of contextual information rises, the complexity of algorithms increases as well, highlighting the data sparsity problem. In this paper, we propose CTITF, a novel tensor factorization model that incorporates constrained user bidirectional trust and implicit feedback. First, CTITF distinguishes the roles of trust networks in terms of the directionality of trust relationships: the trustor and the trustee. CTITF then constructs trust influences and biases for both roles to linearly represent user preferences. Furthermore, CTITF improves the rating prediction function and recommendation objective function based on CP factorization. Finally, CTITF filters out trust relationships where users do not have the same history items. It also uses contextual information to limit the amount of implicit feedback, addressing the issue of high time complexity. Experiments on two real-world datasets illustrate that the proposed model outperforms previous TF-based models and social recommender systems in terms of rating prediction accuracy. In addition, the CTITF model reduces the training time by 50.28% compared to the trustTF model, which also uses trust.

Bidirectional User Throughput Maximization Based on Feedback Reduction in LiFi Networks

Channel adaptive signaling, which is based on feedback, can result in almost any performance metric enhancement. Unlike the radio frequency channel, the optical wireless communication (OWC) channel is relatively deterministic. This feature of OWC channels enables a potential improvement of the bidirectional user throughput by reducing the amount of feedback. Light-Fidelity (LiFi) is a subset of OWCs, and it is a bidirectional, high-speed, and fully networked wireless communication technology where visible light and infrared are used in downlink and uplink, respectively. In this paper, two techniques for reducing the amount of feedback in LiFi cellular networks are proposed: 1) limited-content feedback scheme based on reducing the content of feedback information and 2) limited-frequency feedback scheme based on the update interval. Furthermore, based on the random waypoint mobility model, the optimum update interval, which provides maximum bidirectional user equipment throughput, has been derived. Results show that the proposed schemes can achieve better average overall throughput compared with the benchmark one-bit feedback and full-feedback mechanisms.

Active Scores: Representation and Synchronization in Human–Computer Performance of Popular Music

Computers have the potential to significantly extend the practice of popular music based on steady tempo and mostly determined form. There are significant challenges to overcome, however, due to constraints including accurate timing based on beats and adherence to a form or structure despite possible changes that may occur, possibly even during performance. We describe an approach to synchronization across media that takes into account latency due to communication delays and audio buffering. We also address the problem of mapping from a conventional score with repeats and other structures to an actual performance, which can involve both “flattening” the score and rearranging it, as is common in popular music. Finally, we illustrate the possibilities of the score as a bidirectional user interface in a real-time system for music performance, allowing the user to direct the computer through a digitally displayed score, and allowing the computer to indicate score position back to human performers.

Immersive user environments in rehabilitation robotics and mechatronics

Advances in software, computing, control architectures, and design practice have led to rehabilitation robots with high-bandwidth, bidirectional user interfaces that allow people to interact with mechatronic systems in highly realistic, cognitively and biomechanically coupled scenarios. Immersive and graphic interfaces to real-time systems such as robots require, robust control environments and high-speed computing to provide safe, human-scale motions and interfaces with sufficient quality to be usable in functional environments. This paper illustrates some emerging applications in rehabilitation, spanning assistive technology, simulation/design aids, and smart therapy devices. Examples will be drawn laregly from work done at the VA Palo Alto Rehabilitation R&D Center.