In This paper, the information regarding the corresponding authors is missing. The corresponding authors of the paper should be Shining Ma and Weitao Song.

Augmented reality (AR) technology has great potential in the applications of training, exhibition, and visual guidance, all of which demand precise virtual-real registration in perceived depth. Many AR applications such as navigation and tourism guidance are usually implemented in outdoor environments. However, prior research on depth perception in AR predominantly focused on the indoor environment, characterized by a lower illumination level and more confined space compared to outdoor settings. To address this gap, this paper presented a systematic investigation into the depth perception in outdoor environments. Two experiments were conducted in this study: the first one aimed to explore how to eliminate the bias induced by the floating object and how the knowledge of object height influences the perceived depth. The second experiment examined how ambient luminance affects depth estimation in AR. Our findings revealed an overestimation of perceived depth when participants were unaware of the actual height of the floating object, but an underestimation when they were informed of this information prior to the experiment. Additionally, shadows effectively reduced depth errors regardless of whether participants were informed of the object's height. The second experiment further indicated that, in outdoor environments, reducing ambient luminance significantly improves the accuracy of depth perception in AR.

New network attacks such as Advanced Persistent Threats (APTs) have created an asymmetric dynamic between attackers and defenders. Traditional defense mechanisms typically focus on perimeter security, rendering them ineffective once attackers infiltrate the network. Cyber deception defense, on the other hand, proactively establishes a deceptive environment to manipulate attackers’ perceptions and decisions, thereby delaying, detecting, and potentially thwarting attacks. This paper introduces a novel approach rooted in cyber deception defense, utilizing FPGA technology. By harnessing network packet rewriting techniques on FPGA, this method rapidly generates numerous disguised hosts and ports. The implementation of this approach on an FPGA hardware platform allows for the evaluation of its effectiveness. In simulated environments, the method demonstrates remarkable efficiency in constructing deceptive environments, with a policy query time of approximately 50ns. Transitioning to real-world network scenarios, the prototype system extends the time required for attackers to identify genuine hosts by a factor of 4.5. Moreover, the average delay time of the prototype system in processing 64-byte data packets is approximately 5.68us, showcasing consistent performance across various deception strategies. Crucially, the system’s overhead remains minimal, effectively confounding and deterring attackers while increasing the complexity and cost associated with mounting successful attacks.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Roles of nodes are defined as classes of equivalent nodes. Nodes that have similar local connective patterns may share the same role. As a complementary concept of community, role can also help to recognize real-world entities. For example, it can denote identity or function in social networks. Role has been studied over the past decades, and learning role-based network representations is crucial to many downstream tasks. The important step for role-based network embedding method is extracting features to measure structural similarity instead of proximity. Although some methods have been developed to capture role features to learn structural similarities between nodes, they all design these features of fixed types, such as the global, local, and higher-order features. These features can only represent a certain type of structure, and it is very difficult to model the complex relationship between different scale features in the field of role-based network embedding. Therefore, we propose a novel role-oriented network embedding framework based on adversarial learning between higher-order and local features (ARHOL) to generate powerful role-based node representations. The higher-order features are discrete so we leverage the Auto-Encoder on them to obtain continuous representations. Then we apply the GIN on its outputs to aggregate local information. Finally, we consider the GIN as the generator and design an adversarial game between local features and GIN outputs to integrate these two aspects of features, which can enhance each other and improve the robustness. The extensive experiments on real-world networks demonstrate the superiority and efficiency of our model, and prove the effectiveness of integrating higher-order and local features.

Modeling how information diffuses across social network platforms can be widely used. Recently, researchers have used deep learning methods to model information cascades and forecast their progression without dependence on the hypothesis of the underlying diffusion model. Most of these studies use sequential models (e.g., recurrent neural networks, RNNs) and model cascades of information spread without using the network structure information. However, the network structure information substantially affects information spread, cross-dependence should be considered in cascade modeling, and recurrent neural networks produce poor results on long sequence modeling. To solve these issues, in this paper, we present a new cascade modeling method with the multihead self-attention mechanism. We design an encoder that combines network structure information with multihead self-attention to learn the representations of cascades and consider diverse user dependencies on the network. Experiments are conducted on both synthetic and real-world datasets. The results show that the proposed method performs better on the long sequence cascade prediction than the state-of-the-art methods.