A Utility Framework for Selecting Immersive Interactive Capability and Technology for Virtual Laboratories

There has been an increase in the use of virtual reality (VR) technology in the education community since VR is emerging as a potent educational tool that offers students with a rich source of educational material and makes learning exciting and interactive. With a rise of popularity and market expansion in VR technology in the past few years, a variety of consumer VR electronics have boosted educators and researchers’ interest in using these devices for practicing engineering and science laboratory experiments. However, little is known about how such devices may be well-suited for active learning in a laboratory environment. This research aims to address this gap by formulating a utility framework to help educators and decision-makers efficiently select a type of VR device that matches with their design and capability requirements for their virtual laboratory blueprint. Furthermore, a framework use case is demonstrated by not only surveying five types of VR devices ranging from low-immersive to full-immersive along with their capabilities (i.e., hardware specifications, cost, and availability) but also considering the interaction techniques in each VR device based on the desired laboratory task. To validate the framework, a research study is carried out to compare these five VR devices and investigate which device can provide an overall best-fit for a 3D virtual laboratory content that we implemented based on the interaction level, usability and performance effectiveness

Unsupervised Neural Machine Translation with SMT as Posterior Regularization

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables; meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.Comment: To be presented at AAAI 2019; 9 pages, 4 figure

Study of Primary and Internal Resonance on 3D Free-Free Double-Section Beam

This work investigates the primary resonance and internal resonance of a double-section beam with cubic nonlinearities. This model can be applied in a wide range of engineering problems, such as rocket and missile structures. Even space technology has been developed for decades; several nonlinear properties deserve further study, especially, for the internal resonance. The method of multiple scales (a perturbation technique) is employed to analyze this nonlinear problem. This study focuses on finding the forcing conditions of this 3D double-section beam to trigger the often-ignored internal resonance or prime resonance in rocket structures. A primary resonance is found on a uniform free-free beam at certain flight speed. The three-to-one internal resonance of the double-section beam occurs within the first and the second modes in the diameter ratio of 1/0.75 with the length ratio of 0.33 or 0.51. The semi-analytical results are verified by the time marching numerical method

Towards Explainable Conversational Recommender Systems

Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which poses further challenges for explanations. To better measure explainability in conversational recommender systems (CRS), we propose ten evaluation perspectives based on concepts from conventional recommender systems together with the characteristics of CRS. We assess five existing CRS benchmark datasets using these metrics and observe the necessity of improving the explanation quality of CRS. To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with over 2,000 high-quality rewritten explanations. We compare two baseline approaches to perform explanation generation based on E-ReDial. Experimental results suggest that models trained on E-ReDial can significantly improve explainability while introducing knowledge into the models can further improve the performance. GPT-3 in the in-context learning setting can generate more realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial can better generate clear reasons for recommendations based on user preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial