In the problem of dispersion, a set of mobile robots, starting from one or multiple source nodes in a network, must relocate themselves in such a way that there is at most one robot present at any node of the graph. Most of the prior works on dispersion assume local communication between robots: any two robots, while located on the same node, can communicate by exchanging messages of arbitrary size. A recent paper by Gorain et al. [4] has shown that this kind of local communication is not necessary to achieve dispersion. They have shown that dispersion can be achieved using very limited local knowledge, where a robot can sense the following local information at any node: A) Is the robot alone at a node in a round? B) Do the number of robots at a node in the current round have increased compared to the previous round? and C) Do the number of robots at a node in the current round have decreased compared to the previous round? The authors have proposed an algorithm using which a set of co-located robots with access to the above local information achieved dispersion. In this paper, we show the existence of an algorithm for dispersion in a weaker model than that is considered in [4]. Specifically, we show that the knowledge of (A) and (C) are enough to achieve dispersion. Moreover, our algorithm works even if the robots do not wake up at the same time.
Transformer-based architectures have recently emerged as powerful models for 3D medical image segmentation, with UNETR demonstrating notable success by leveraging self-attention to capture global contextual information. Despite its effectiveness, UNETR still faces challenges in interpretability and computational efficiency. To address these limitations, we propose UKANETR, a novel UNET variant that employs kansformers, i.e., transformers equipped with Kolmogorov–Arnold Network multilayer perceptrons (KAN-MLPs). By integrating KAN-MLPs into the transformer blocks, UKANETR enhances model interpretability while improving its ability to represent complex nonlinear functions with fewer parameters. In addition, UKANETR incorporates a squeeze-and-excitation mechanism within the skip connection pathways to explicitly capture channel interdependencies. This design improves feature recalibration, allowing the network to emphasize informative channels and suppress less relevant ones, thereby strengthening multi-level feature fusion between encoder and decoder stages. The proposed UKANETR was rigorously evaluated on the BRaTS 2020 brain tumor MRI dataset. Comparative analysis against the traditional UNETR and other state-of-the-art models shows that UKANETR consistently achieves superior performance in multi-level brain tumor segmentation, demonstrating improvements in Dice score, sensitivity, and boundary delineation accuracy. These results highlight the potential of integrating kansformers and channel attention mechanisms into transformer-based UNET models, offering a more interpretable and efficient solution for 3D medical image segmentation tasks.
Phishing attacks are on the rise in current society where attackers try to steal sensitive information from the user using any means necessary. Majority of the attacks leverage electronic mails to trick unsuspecting users to click a link or urge a sense of urgency. Thus this research work aims to explore key features of phishing emails for the detection of the phishing attacks. The main focus was to apply Exploratory Data Analysis (EDA) on key features like URL Patterns, timestamps, sentiment of sender, grammar and TF-IDF (Term Frequency-Inverse Document Frequency) which can be leveraged in detection of phishing attacks. Using these features, machine learning models like Random Forest (RF), XG Boost (eXtreme Gradient Boosting) were evaluated on the dataset mentioned in Sect. 3.1 and it was found that XG Boost provided the False Positive Rate of just 1.76
Reisig’s Algebraic Petri nets are highly expressive but rarely based on a strict algebraic framework. We present an effective translation using the declarative language Maude, which employs rewriting semantics. By exploring two definitions, we tackle modeling challenges from Maude’s pattern-matching operational semantics. We demonstrate the advantages of using rewritable terms as active tokens, as supported by Maude, with examples including an adaptive Multilevel Feedback Queue scheduling.
Accurate segmentation of colorectal polyps is critical for the early detection and prevention of colorectal cancer, yet remains challenging due to significant variability in polyp shape, size, texture, and boundary ambiguity. To address these challenges, we propose FusePolyp, a novel Complementary Dual Encoder Framework that integrates the global semantic modeling ability of transformers with the spatial precision of convolutional features. The architecture leverages a pretrained PVTv2-B3 encoder to capture rich hierarchical semantic representations, complemented by a custom ReSEVM encoder that focuses on local detail through Residual connections, Squeeze-and-Excitation recalibration, and lightweight Visual State-Space Mamba blocks. To effectively combine features from both branches, we introduce the Cross-Mamba Channel-Spatial Fusion (CMCSF) module, which dynamically modulates channel and spatial dependencies while modeling long-range interactions. The fused features are further enhanced through a dual-branch bottleneck consisting of a lightweight transformer and an ASPP module, while a gated decoder with skip connections enables precise boundary reconstruction. Extensive experiments conducted on five benchmark datasets–Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, ETIS, and ColonDB–demonstrate that FusePolyp consistently outperforms state-of-the-art methods, achieving the highest mIoU, Dice, and F _2 scores across all datasets. Notably, FusePolyp delivers improvements of up to 2.0–3.5 _2 compared to the best competing models, highlighting its superior segmentation accuracy, robustness, and generalization capability across diverse colonoscopic imaging conditions.
