MuSimA: A Tool with Multi-modal Input for Generating Bespoke ABAC Datasets
- URL: http://arxiv.org/abs/2604.10501v1
- Date: Sun, 12 Apr 2026 07:27:48 GMT
- Title: MuSimA: A Tool with Multi-modal Input for Generating Bespoke ABAC Datasets
- Abstract summary: MuSimA is a web-based tool for generating ABAC datasets with user-specified probability distributions of attribute input values.<n>For studying scalability of algorithms and methods related to ABAC, data can be generated for varying sizes and complexities.
- Score: 0.038180404292108366
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in research on Attribute-based Access Control (ABAC) has led to the development of several ingenious methods for representing and enforcing organizational security policies. However, so far little effort has been spent towards building a tool for generating large-scale synthetic datasets that can be used to test the developed ABAC systems. In this paper, we address this shortcoming by building MuSimA - a web-based tool for generating ABAC datasets with user-specified probability distributions of attribute values. It supports multi-modal input, i.e., users can provide specifications either as a structured JSON file or as a combination of a minimal JSON along with hand-drawn distribution sketches. In the latter case, a Large Language Model is used to automatically extract appropriate distribution parameters from the sketches. The generated synthetic ABAC data matching the input specifications can be downloaded by the user. For studying scalability of algorithms and methods related to ABAC, data can be generated for varying sizes and complexities. We make MuSimA freely available for use by the research community.
Related papers
- MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning [80.41987492855971]
We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM)<n>Within this architecture, multi-modal data pertinent to social intelligence is precisely localized.<n>With around 30% of training data from IntentTrain, we achieve state-of-the-art results.
arXiv Detail & Related papers (2026-06-10T12:44:02Z) - Steerable Instruction Following Coding Data Synthesis with Actor-Parametric Schema Co-Evolution [30.919665619254]
We propose IFCodeEvolve, a framework for synthesizing large-scale instruction-paired coding data.<n>We show that IFCodeEvolve significantly boosts base model performance, with our 32B model achieving parity with proprietary SOTA models.<n>We also contribute IFCodeBench, a comprehensive human-verified benchmark equipped with solutions and robust AST-based verification.
arXiv Detail & Related papers (2026-02-27T21:31:41Z) - Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents [85.02904078131682]
We introduce the agent data protocol (ADP), a light-weight representation language that serves as an "interlingua" between agent datasets.<n> ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic.<n>All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.
arXiv Detail & Related papers (2025-10-28T17:53:13Z) - AI for Distributed Systems Design: Scalable Cloud Optimization Through Repeated LLMs Sampling And Simulators [3.1594665317979698]
We explore AI-driven distributed-systems policy design by combining code generation from large language models with deterministic verification in a domain-specific simulator.<n>We report preliminary results on throughput improvements across multiple models.<n>We conjecture that AI will be crucial for scaling this methodology by helping to bootstrap new simulators.
arXiv Detail & Related papers (2025-10-20T16:10:24Z) - Comprehensive Evaluation of Prototype Neural Networks [0.6967006044904097]
Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning.<n>In this paper, we perform an in-depth analysis of a set of prominent prototype models including ProtoPNet, ProtoPool and PIPNet.<n>We propose several new metrics to further complement the analysis of model interpretability.
arXiv Detail & Related papers (2025-07-09T13:08:21Z) - What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities [56.646832992178105]
We introduce OmniBench, a cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity.<n>We present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.<n>Our dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91% human acceptance rate.
arXiv Detail & Related papers (2025-06-10T15:59:38Z) - CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation [51.2289822267563]
We propose a method for generating synthetic datasets, given a small number of user-written few-shots that demonstrate the task to be performed.<n>We demonstrate that CRAFT can efficiently generate large-scale task-specific training datasets for four diverse tasks: biology, medicine, and commonsense question-answering (QA)<n>Our experiments show that CRAFT-based models outperform or match general LLMs on QA tasks, while exceeding models trained on human-curated summarization data by 46 preference points.
arXiv Detail & Related papers (2024-09-03T17:54:40Z) - LAMBDA: A Large Model Based Data Agent [7.240586338370509]
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system.<n>LAMBDA is designed to address data analysis challenges in data-driven applications.<n>It has the potential to enhance data analysis paradigms by seamlessly integrating human and artificial intelligence.
arXiv Detail & Related papers (2024-07-24T06:26:36Z) - Zero-shot Composed Text-Image Retrieval [72.43790281036584]
We consider the problem of composed image retrieval (CIR)
It aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability.
arXiv Detail & Related papers (2023-06-12T17:56:01Z) - TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations.
We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
arXiv Detail & Related papers (2023-05-19T10:11:21Z) - Creating Synthetic Datasets for Collaborative Filtering Recommender
Systems using Generative Adversarial Networks [1.290382979353427]
Research and education in machine learning needs diverse, representative, and open datasets to handle the necessary training, validation, and testing tasks.
To feed this research variety, it is necessary and convenient to reinforce the existing datasets with synthetic ones.
This paper proposes a Generative Adversarial Network (GAN)-based method to generate collaborative filtering datasets.
arXiv Detail & Related papers (2023-03-02T14:23:27Z) - Multi-layer Optimizations for End-to-End Data Analytics [71.05611866288196]
We introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach.
IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language.
We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and specialization by several orders of magnitude for linear regression and regression tree models over several relational datasets.
arXiv Detail & Related papers (2020-01-10T16:14:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.