Vision Transformers (ViTs) and their variants have been increasingly adopted in intelligent systems. However, the security implications of partially exposing self-attention information in ViTs remain largely underexplored. Existing adversarial attacks on ViTs typically rely on white-box assumptions with unrealistic access or black-box settings with limited effectiveness, limiting their relevance to restricted deployment conditions. In this paper, we investigate a plausible gray-box threat model motivated by interpretability-oriented deployment scenarios, where limited attention-related information may be observable through interpretability or analysis interfaces. Our analysis suggests that such partial attention exposure may introduce a previously underexplored vulnerability in Vision Transformers. Based on this observation, we propose Targeted Patch Perturbation (TPP), an attention-guided patch perturbation framework tailored for restricted gray-box settings that localizes semantically important patches and restricts perturbations to these regions. The proposed method enables effective adversarial attacks without requiring gradient access to the target model during attack execution. Experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate that TPP consistently outperforms strong black-box baselines under the same perturbation budget. Further evaluations across multiple ViT architectures reveal that attention-guided patch selection can substantially improve attack transferability under restricted-information settings, suggesting that semantically aligned perturbation localization may affect shared semantic representations across transformer models and influence the adversarial robustness of Vision Transformer systems. The source code is available at: https://github.com/BaoChau-Ho/TGP_adv

In the domain of Temporal Knowledge Graph Completion, existing models often struggle with efficiently capturing the intricate temporal dynamics and interactions within knowledge graphs.To address these challenges, this paper introduces T-WinG, a novel approach that incorporates the Swin Transformer architecture, renowned for its efficacy in hierarchical representation learning.By integrating SPLIME's preprocessing techniques and refining the Swin Transformer's token mixer, T-WinG substantially improves performance.Specifically, our model demonstrates a performance improvement of up to 20% in accuracy metrics such as Mean Reciprocal Rank (MRR) and Hits@K, across four benchmark datasets compared to the best-performing baseline models.These results not only underscore T-WinG's ability to handle dynamic temporal data but also highlight its potential to address the pressing needs of real-world applications requiring accurate and timely insights from knowledge graphs.

We consider the following problem: Given n red points, m green points and p blue points on a plane, find a trio of red-green-blue points such that the distance between them is smallest. This problem could be solved by an exhaustive search algorithm: test all trios of 3 different colored points and find the closest trio. However, this algorithm has the complexity of O(N 3 ), N = max(n, p, q). In our previous research, we already showed a heuristic algorithm with a good complexity to solve this problem based on 2D Voronoi diagram. In this paper, we introduce a better heuristic algorithm for solving this problem. This new heuristic algorithm has the same complexity but it performs much better than the old one.