Tootfinder

WebGuard :Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
Ye Tian, Zhang Yumin, Yifan Jia, Jianguo Sun, Yanbin Wang
https://arxiv.org/abs/2506.19356

WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
URL+HTML feature fusion shows promise for robust malicious URL detection, since attacker artifacts persist in DOM structures. However, prior work suffers from four critical shortcomings: (1) incomplete URL modeling, failing to jointly capture lexical patterns and semantic context; (2) HTML graph sparsity, where threat-indicative nodes (e.g., obfuscated scripts) are isolated amid benign content, causing signal dilution during graph aggregation; (3) unidirectional analysis, ignoring URL-HTML feat…

Revisiting the Effect of Grid-Following Converter on Frequency Dynamics -- Part I: Center of Inertia
Jiahao Liu, Cheng Wang, Tianshu Bi
https://arxiv.org/abs/2507.15358

Revisiting the Effect of Grid-Following Converter on Frequency Dynamics -- Part I: Center of Inertia
Understanding the impact of grid-following (GFL) converters on system frequency dynamics is crucial, from both the center of inertia (COI) and frequency spatial variation perspectives. Part I of this series clarifies the mechanisms by which GFLs influence COI frequency dynamics. A multi-generator model of the power system with GFLs is developed, incorporating the local dynamics of GFLs and their interaction with synchronous generators via virtual tie lines. By aggregating the multi-generator mo…

Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yanchao Tan, Yu Rong, Hong Cheng, Lingling Yi
https://arxiv.org/abs/2507.15395

Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
In real-world recommendation scenarios, users typically engage with platforms through multiple types of behavioral interactions. Multi-behavior recommendation algorithms aim to leverage various auxiliary user behaviors to enhance prediction for target behaviors of primary interest (e.g., buy), thereby overcoming performance limitations caused by data sparsity in target behavior records. Current state-of-the-art approaches typically employ hierarchical design following either cascading (e.g., vi…

Distributed Kaplan-Meier Analysis via the Influence Function with Application to COVID-19 and COVID-19 Vaccine Adverse Events
Malcolm Risk, Xu Shi, Lili Zhao
https://arxiv.org/abs/2507.14351

Distributed Kaplan-Meier Analysis via the Influence Function with Application to COVID-19 and COVID-19 Vaccine Adverse Events
During the COVID-19 pandemic, regulatory decision-making was hampered by a lack of timely and high-quality data on rare outcomes. Studying rare outcomes following infection and vaccination requires conducting multi-center observational studies, where sharing individual-level data is a privacy concern. In this paper, we conduct a multi-center observational study of thromboembolic events following COVID-19 and COVID-19 vaccination without sharing individual-level data. We accomplish this by devel…

Multi-Wavelength Dust Characterization of the HL Tau Disk and Implications for Planet Formation
Takahiro Ueda, Sean M. Andrews, Carlos Carrasco-Gonz\'alez, Osmar M. Guerra-Alvarado, Satoshi Okuzumi, Ryo Tazaki, Akimasa Kataoka
https://arxiv.org/abs/2507.14443

Multi-Wavelength Dust Characterization of the HL Tau Disk and Implications for Planet Formation
We present a comprehensive analysis of the HL Tau dust disk by modeling its intensity profiles across six wavelengths (0.45 to 7.9 mm) with a resolution of 0.05 arcsec ($\sim7$ au). Using a Markov Chain Monte Carlo (MCMC) approach, we constrain key dust properties including temperature, surface density, maximum grain size, composition, filling factor, and size distribution. The full fitting, with all parameters free, shows a preference for organics-rich dust with a low filling factor in the out…

Bipartite Tur\'an numbers via edge-gluing
Zihao Jin, Sean Longbrake, Liana Yepremyan
https://arxiv.org/abs/2507.16804 https://arx…

Bipartite Turán numbers via edge-gluing
In 1984, Erdős and Simonovits asked the following: given a bipartite graph $H$, do there exist constants $0 \leq α< 1$ and $β, C > 0$ such that any graph $G$ on $n$ vertices and $pn^2\geq C n^{1+ α}$ edges contains at least $βn^{\mathrm{v}(H)} p^{\mathrm{e}(H)}$ copies of $H$? We show that edge-gluing preserves the satisfiability of this conjecture under some mild symmetry conditions. Namely, if two graphs $H_1$ and $H_2$ satisfy this conjecture, and if furthermore, gluing them along a f…

Hard-Stop Synthesis for Multi-DOF Compliant Mechanisms
Dean Chen, Armin Pomeroy, Brandon T. Peterson, Will Flanagan, He Kai Lim, Alexandra Stavrakis, Nelson F. SooHoo, Jonathan B. Hopkins, Tyler R. Clites
https://arxiv.org/abs/2507.13455

Hard-Stop Synthesis for Multi-DOF Compliant Mechanisms
Compliant mechanisms have significant potential in precision applications due to their ability to guide motion without contact. However, an inherent vulnerability to fatigue and mechanical failure has hindered the translation of compliant mechanisms to real-world applications. This is particularly challenging in service environments where loading is complex and uncertain, and the cost of failure is high. In such cases, mechanical hard stops are critical to prevent yielding and buckling. Convent…

EduThink4AI: Translating Educational Critical Thinking into Multi-Agent LLM Systems
Xinmeng Hou, Zhouquan Lu, Wenli Chen, Hai Hu, Qing Guo
https://arxiv.org/abs/2507.15015

EduThink4AI: Translating Educational Critical Thinking into Multi-Agent LLM Systems
Large language models (LLMs) have demonstrated significant potential as educational tutoring agents, capable of tailoring hints, orchestrating lessons, and grading with near-human finesse across various academic domains. However, current LLM-based educational systems exhibit critical limitations in promoting genuine critical thinking, failing on over one-third of multi-hop questions with counterfactual premises, and remaining vulnerable to adversarial prompts that trigger biased or factually in…

Constructions and List Decoding of Sum-Rank Metric Codes Based on Orthogonal Spaces over Finite Fields
Xuemei Liu, Jiarong Zhang, Gang Wang
https://arxiv.org/abs/2507.16377 http…

Constructions and List Decoding of Sum-Rank Metric Codes Based on Orthogonal Spaces over Finite Fields
Sum-rank metric codes, as a generalization of Hamming codes and rank metric codes, have important applications in fields such as multi-shot linear network coding, space-time coding and distributed storage systems. The purpose of this study is to construct sum-rank metric codes based on orthogonal spaces over finite fields, and calculate the list sizes outputted by different decoding algorithms. The following achievements have been obtained. In this study, we construct a cyclic orthogonal grou…

Gaze-supported Large Language Model Framework for Bi-directional Human-Robot Interaction
Jens V. R\"uppel, Andrey Rudenko, Tim Schreiter, Martin Magnusson, Achim J. Lilienthal
https://arxiv.org/abs/2507.15729

Gaze-supported Large Language Model Framework for Bi-directional Human-Robot Interaction
The rapid development of Large Language Models (LLMs) creates an exciting potential for flexible, general knowledge-driven Human-Robot Interaction (HRI) systems for assistive robots. Existing HRI systems demonstrate great progress in interpreting and following user instructions, action generation, and robot task solving. On the other hand, bi-directional, multi-modal, and context-aware support of the user in collaborative tasks still remains an open challenge. In this paper, we present a gaze- …