Shengzhe Hou

Shengzhe Hou, Bruce H. Thomas

With the rapid development of Virtual Reality (VR) technology, the research of User Interface (UI), especially menus, in the VR environment has attracted more and more attention. However, it is very tedious for researchers to develop UI from scratch or modify existing functions and there are no easy-to-use tools for efficient development. This paper aims to present VRMenuDesigner, a flexible and modular toolkit for automatically generating/modifying VR menus. This toolkit is provided as open-source library and easy to extend to adapt to various requirements. The main contribution of this work is to organize the menus and functions with object-oriented thinking, which makes the system very understandable and extensible. VRMenuDesigner includes two key tools: Creator and Modifier for quickly generating and modifying elements. Moreover, we developed several built-in menus and discussed their usability. After a brief review and taxonomy of 3D menus, the architecture and implementation of the toolbox are introduced.

Shengzhe Hou, Xinming Lu, Tianyu Zhang et al.

Underground mining robots are increasingly operated in virtual environments (VEs) for training, planning, and digital-twin applications, where reliable kinematics is essential for avoiding hazardous in-situ trials. Unlike typical open-chain industrial manipulators, mining robots are often closed-chain mechanisms driven by linear actuators and involving planar four-bar linkages, which makes both kinematics modeling and real-time solving challenging. We present \emph{MineRobot}, a unified framework for modeling and solving the kinematics of underground mining robots in VEs. First, we introduce the Mining Robot Description Format (MRDF), a domain-specific representation that parameterizes kinematics for mining robots with native semantics for actuators and loop closures. Second, we develop a topology-processing pipeline that contracts four-bar substructures into generalized joints and, for each actuator, extracts an Independent Topologically Equivalent Path (ITEP), which is classified into one of four canonical types. Third, leveraging ITEP independence, we compose per-type solvers into an actuator-centered sequential forward-kinematics (FK) pipeline. Building on the same decomposition, we formulate inverse kinematics (IK) as a bound-constrained optimization problem and solve it with a Gauss--Seidel-style procedure that alternates actuator-length updates. By converting coupled closed-loop kinematics into a sequence of small topology-aware solves, the framework avoids robot-specific hand derivations and supports efficient computation. Experiments demonstrate that MineRobot provides the real-time performance and robustness required by VE applications.

Shuai Jiang, Sayaka Kamei, Chen Li et al.

The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it usually have difficulty in interpretation and low robustness. This paper proposes a visual interpretation-based self-improving classification model with a combination of virtual adversarial training (VAT) and BERT models to address the above problems. Specifically, a fine-tuned BERT model is used as a classifier to classify the sentiment of the text. Then, the predicted sentiment classification labels are used as part of the input of another BERT for spam classification via a semi-supervised training manner using VAT. Additionally, visualization techniques, including visualizing the importance of words and normalizing the attention head matrix, are employed to analyze the relevance of each component to classification accuracy. Moreover, brand-new features will be found in the visual analysis, and classification performance will be improved. Experimental results on Twitter's tweet dataset demonstrate the effectiveness of the proposed model on the classification task. Furthermore, the ablation study results illustrate the effect of different components of the proposed model on the classification results.