Tootfinder

Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets
Avishek Bhandari
https://arxiv.org/abs/2507.08065 https://

Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets
This paper introduces a novel framework for analyzing systemic risk in financial markets through multi-scale network dynamics using Model Context Protocol (MCP) for agent communication. We develop an integrated approach that combines transfer entropy networks, agent-based modeling, and wavelet decomposition to capture information flows across temporal scales implemented in the MCPFM (Model Context Protocol Financial Markets) R package. Our methodology enables heterogeneous financial agents incl…

Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users
Haowei Yang, Yushang Zhao, Sitao Min, Bo Su, Chao Yao, Wei Xu
https://arxiv.org/abs/2509.09066

Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users
The cold-start user issue further compromises the effectiveness of recommender systems in limiting access to the historical behavioral information. It is an effective pipeline to optimize instructional prompts on a few-shot large language model (LLM) used in recommender tasks. We introduce a context-conditioned prompt formulation method P(u,\ Ds)\ \rightarrow\ R\widehat, where u is a cold-start user profile, Ds is a curated support set, and R\widehat is the predicted ranked list of items. Based…

Dependency on Meta AI Chatbot in Messenger Among STEM and Non-STEM Students in Higher Education
Hilene E. Hernandez, Rhiziel P. Manalese, Roque Francis B. Dianelo, Jaymark A. Yambao, Almer B. Gamboa, Lloyd D. Feliciano, Mike Haizon M. David, Freneil R. Pampo, John Paul P. Miranda
https://arxiv.org/abs/2507.21059

Dependency on Meta AI Chatbot in Messenger Among STEM and Non-STEM Students in Higher Education
To understand the potential dependency of tertiary students regarding Meta AI in the academic context. This descriptive cross-sectional study surveyed 872 tertiary students from public and private institutions in Luzon, Philippines. Demographic information and perceptions on Meta AI dependency based on existing literature were collected. Descriptive statistics were used to summarize the data and differences between STEM and non-STEM students were analyzed using the Mann-Whitney U test. The resu…

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
Negar Foroutan, Angelika Romanou, Matin Ansaripour, Julian Martin Eisenschlos, Karl Aberer, R\'emi Lebret
https://arxiv.org/abs/2506.15594

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-language large models (VLLMs) have demonstrated improvements across various tasks, their effectiveness in processing long-context vision inputs remains unclear. This paper introduces WikiMixQA, a benchmark comprising 1,000 multiple-choice questions (MCQs) designed to evaluate cross-…

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Siyuan Li, Ruitong Liu, Yan Wen, Te Sun
https://arxiv.org/abs/2506.23141

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowledge graphs, often introduce noise and suffer from information dilution or over-smoothing by indiscriminately aggregating information from all neighboring edges. To address this challenge, we propose a semantic-aware relational message passing. A core innovation of this framework …

INTACT: Compact Storage of Data Streams in Mobile Devices to Unlock User Privacy at the Edge
R\'emy Raes (SPIRALS), Olivier Ruas (SPIRALS), Adrien Luxey-Bitri (SPIRALS), Romain Rouvoy (SPIRALS)
https://arxiv.org/abs/2506.21998

INTACT: Compact Storage of Data Streams in Mobile Devices to Unlock User Privacy at the Edge
Data streams produced by mobile devices, such as smartphones, offer highly valuable sources of information to build ubiquitous services. Such data streams are generally uploaded and centralized to be processed by third parties, potentially exposing sensitive personal information. In this context, existing protection mechanisms, such as Location Privacy Protection Mechanisms (LPPMs), have been investigated. Alas, none of them have actually been implemented, nor deployed in real-life, in mobile d…

Orchestration of Music by Grammar Systems
Jozef Maki\v{s} (Faculty of Information Technology, Brno University of Technology), Alexander Meduna (Faculty of Information Technology, Brno University of Technology), Zbyn\v{e}k K\v{r}ivka (Faculty of Information Technology, Brno University of Technology)
https://arxiv.org/abs/2507.15314

Orchestration of Music by Grammar Systems
This application-oriented study concerns computational musicology, which makes use of grammar systems. We define multi-generative rule-synchronized scattered-context grammar systems (without erasing rules) and demonstrates how to simultaneously make the arrangement of a musical composition for performance by a whole orchestra, consisting of several instruments. Primarily, an orchestration like this is illustrated by examples in terms of classical music. In addition, the orchestration of jazz co…

eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
Paul R. B. Houssel, Siamak Layeghy, Priyanka Singh, Marius Portmann
https://arxiv.org/abs/2507.16241

eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems (NIDS) by leveraging Large Language Models (LLMs). In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Prompt Augmenter. This module extracts contextual information and Cyber Threat Intelligence (CTI)-related knowledge from these flows. This enriched, context-specific data is then integrated with an input…