Abstract In recent years, the rapid development of the intelligent connected vehicle industry has highlighted the critical importance of in-vehicle network security. The Controller Area Network(CAN) protocol is widely used as the core communication framework within in-vehicle systems. However, the CAN protocol lacks encryption and authentication mechanisms, making the network vulnerable to various security threats. Additionally, the rapid advancement of Vehicle-to-Everything communication has increased the frequency of interactions between vehicles and external environments, further exacerbating the security risks of the Internet of Vehicles (IoV) and highlighting the necessity of effective intrusion detection methods. Existing detection methods often face challenges like slow detection speeds and low accuracy. To overcome these limitations, this paper proposes a novel lightweight neural network model that integrates the Mamba architecture with Convolutional Neural Networks (CNNs) for IoV intrusion detection. In the in-vehicle scenario, a two-stage detection method combines single-message detection with message sequence detection. Single-message detection processes each message individually, enabling fine-grained intrusion detection. In contrast, message sequence detection leverages the periodic characteristics of CAN bus messages to detect anomalies in consecutive message sequences. In a complex external network environment, we employ a stacking ensemble approach and knowledge distillation to enhance and compress the model, resulting in a compact, high-performance, lightweight solution. Experimental results on four publicly available datasets show that our method has good detection performance and provides an effective solution to the security challenges in the IoV.

Raycasting is a common technique for selecting targets in virtual reality, yet selecting small, distant moving targets remains a challenge. Inspired by the comet mechanism, we propose TrailTag, which enhances selection by adding a Trail to each target, dynamically adjusting its activation area based on speed and size. Additionally, a static Tag is attached, allowing selection via either the Trail or Tag depending on the scenario. We define the design criteria for both the Trail and Tag, implement visual feedback, and determine the optimal trail width through experiments. Comparative studies with various 3D selection techniques show that TrailTag reduces selection time by 59% and error rate by 62% compared to traditional Raycasting, significantly improving user experience. Finally, we discuss potential optimizations and explore broader applications of TrailTag in dynamic environments.

A safety boundary wall in VR is a virtual barrier that defines a safe area, allowing users to navigate and interact without safety concerns. However, existing implementations neglect to utilize the safety boundary wall's large surface for displaying interactive information. In this work, we propose the BoundaryScreen technique based on the "walking outward" metaphor to add interactivity to the safety boundary wall. Specifically, we augment the safety boundary wall by placing the home screen on it. To summon the home screen, the user only needs to walk outward until it appears. Results showed that (i) participants significantly preferred BoundaryScreen in the outermost two-step-wide ring-shaped section of a circular safety area; and (ii) participants exhibited strong "behavioral inertia" for walking, i.e., after completing a routine activity involving constant walking, participants significantly preferred to use the walking-based BoundaryScreen technique to summon the home screen.

Text input is one of the common interaction tasks in virtual environments. However, current inputting methods (e.g., laser-input: aim-and-shoot technique) have many limitations, such as inefficiency, lack of precision, and fatigue of long-text inputting. We propose a Climbing Keyboard method to allow easier, faster, and more accurate text input, and use tilt instead of precise aiming. The selected target changes from a specific letter to a group of letters with such a tilt-based interaction based on the QWERTY layout, which aims to reduce the learning cost, especially for novice users. Meanwhile, expert users can focus on the screen without looking at the keyboard. We designed three user studies to evaluate the performance of proposed method, including the verification of the usability of the tilt interaction method in the first study, optimization of the tilt angle range in the second study, and evaluation of the learning curve of Climbing keyboard in the last study. Our results showed that participants can reach 16.48 words per minute after an hours of training.

Interactions with digital devices during social settings can reduce social engagement and interrupt conversations. To overcome these drawbacks, we designed ParaGlassMenu, a semi-transparent circular menu that can be displayed around a conversation partner’s face on Optical See-Through Head-Mounted Display (OHMD) and interacted subtly using a ring mouse. We evaluated ParaGlassMenu with several alternative approaches (Smartphone, Voice assistant, and Linear OHMD menus) by manipulating Internet-of-Things (IoT) devices in a simulated conversation setting with a digital partner. Results indicated that the ParaGlassMenu offered the best overall performance in balancing social engagement and digital interaction needs in conversations. To validate these findings, we conducted a second study in a realistic conversation scenario involving commodity IoT devices. Results confirmed the utility and social acceptance of the ParaGlassMenu. Based on the results, we discuss implications for designing attention-maintaining subtle interaction techniques on OHMDs.

Seismic data denoising has gained much attention from scholars as a crucial part of seismic data processing. With the development of deep learning technology, numerous algorithms well employed in natural image denoising have been used for seismic data denoising. However, seismic data are crucially an array of geophone vibration signals, which have numerous unique structural properties compared with natural images. Thus, employing the traditional image algorithms rather than those developed specifically for seismic data is inadequate to extract all seismic features. Additionally, compared with natural images, it is difficult to acquire noise-free ground truth, which restricts the use of supervised deep learning approaches in seismic data denoising. To this end, a self-supervised inter-trace seismic data denoising network (STSNet) that requires only one seismic section for random noise attenuation is proposed. Furthermore, we adopt a single trace vibration signal as the basic unit and fully consider the characteristics of seismic signals. This is the first time to introduce a self-learning spatial attention mechanism among seismic traces to focus on the noise components, which prompts the network’s fitting performance. Different comparative experiments demonstrate that our approach can achieve exceptional denoising performance even if only observing a single seismic section. Additionally, the ablation experiments also confirm the efficiency of spatial attention.

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

Bayesian network classifiers (BNCs) are powerful tools to mine statistical knowledge from data and infer under conditions of uncertainty. However, most of the traditional BNCs focus on mining the dependency relationships existed in labeled data while neglecting the information hidden in unlabeled d ata, which may result in the biased decision boundaries. To address this issue, we introduce a new order-based greedy search heuristic based on mutual information for building efficient structures in tree-augmented naive Bayes (TAN), which is a highly accurate learner while maintaining simplicity and efficiency. Target learning is used to dynamically describe the dependency relationships in each unlabeled test instance. Extensive experimental results on UCI (University of California at Irvine) machine learning repository demonstrate that our proposed algorithm is a competitive alternative to state-of-the-art classifiers like weighted averaged TAN and k-dependence Bayesian classifier, as well as Random forest.

The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.