Tootfinder

Incorporating Taxonomies of Cyber Incidents Into Detection Networks for Improved Detection Performance
Ryan Warnick
https://arxiv.org/abs/2508.10187 https://

Incorporating Taxonomies of Cyber Incidents Into Detection Networks for Improved Detection Performance
Many taxonomies exist to organize cybercrime incidents into ontological categories. We examine some of the taxonomies introduced in the literature; providing a framework, and analysis, of how best to leverage different taxonomy structures to optimize performance of detections targeting various types of threat-actor behaviors under the umbrella of precision and recall. Networks of detections are studied, and results are outlined showing properties of networks of interconnected detections. Some i…

Exploring Molecular Odor Taxonomies for Structure-based Odor Predictions using Machine Learning
Akshay Sajan, Stijn Sluis, Reza Haydarlou, Sanne Abeln, Pasquale Lisena, Raphael Troncy, Caro Verbeek, Inger Leemans, Halima Mouhib
https://arxiv.org/abs/2508.09217

Exploring Molecular Odor Taxonomies for Structure-based Odor Predictions using Machine Learning
One of the key challenges to predict odor from molecular structure is unarguably our limited understanding of the odor space and the complexity of the underlying structure-odor relationships. Here, we show that the predictive performance of machine learning models for structure-based odor predictions can be improved using both, an expert and a data-driven odor taxonomy. The expert taxonomy is based on semantic and perceptual similarities, while the data-driven taxonomy is based on clustering co…

The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube
Rongyi Chen, Ziyan Xin, Qing Xiao, Ruiwei Xiao, Jingjia Xiao, Bingbing Zhang, Hong Shen, Zhicong Lu
https://arxiv.org/abs/2509.10957

The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube
The digital transformation of religious practice has reshaped how billions of people engage with spiritual content, with video-sharing platforms becoming central to contemporary religious communication. Yet HCI research lacks systematic understanding of how narrative and visual elements create meaningful spiritual experiences and foster viewer engagement. We present a mixed-methods study of religious videos on YouTube across major religions, developing taxonomies of narrative frameworks, visual…

Acoustic Overspecification in Electronic Dance Music Taxonomy
Weilun Xu, Tianhao Dai, Oscar Goudet, Xiaoxuan Wang
https://arxiv.org/abs/2509.11474 https://…

Acoustic Overspecification in Electronic Dance Music Taxonomy
Electronic Dance Music (EDM) classification typically relies on industry-defined taxonomies with numerous subgenres, yet the acoustic basis for these distinctions remains unclear. Current approaches use supervised learning with prescribed genre labels, assuming their validity without systematic evaluation. In this paper, we propose an unsupervised approach to discover the natural acoustic structure of EDM independent of commercial labels. Our method combines novel tempogram-based features captu…

Extracting Document Relations from Search Corpus by Marginalizing over User Queries
Yuki Iwamoto, Kaoru Tsunoda, Ken Kaneiwa
https://arxiv.org/abs/2507.10726

Extracting Document Relations from Search Corpus by Marginalizing over User Queries
Understanding relationships between documents in large-scale corpora is essential for knowledge discovery and information organization. However, existing approaches rely heavily on manual annotation or predefined relationship taxonomies. We propose EDR-MQ (Extracting Document Relations by Marginalizing over User Queries), a novel framework that discovers document relationships through query marginalization. EDR-MQ is based on the insight that strongly related documents often co-occur in results…

Crosslisted article(s) found for math.PR. https://arxiv.org/list/math.PR/new
[1/1]:
- Incorporating Taxonomies of Cyber Incidents Into Detection Networks for Improved Detection Perfor...
Ryan Warnick

A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
Boyang Yang, Zijian Cai, Fengling Liu, Bach Le, Lingming Zhang, Tegawend\'e F. Bissyand\'e, Yang Liu, Haoye Tian
https://arxiv.org/abs/2506.23749

A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
Large language models (LLMs) are reshaping automated program repair (APR). We categorize the recent 63 LLM-based APR systems published from January 2022 to June 2025 into four paradigms, and show how retrieval- or analysis-augmented contexts strengthen any of them. This taxonomy clarifies key trade-offs: fine-tuning delivers strong task alignment at high training cost; prompting enables rapid deployment but is limited by prompt design and context windows; procedural pipelines offer reproducible…

Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development
Sanjana Gautam, Mohit Chandra, Ankolika De, Tatiana Chakravorti, Girik Malik, Munmun De Choudhury
https://arxiv.org/abs/2508.06849

Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development
Lived experiences fundamentally shape how individuals interact with AI systems, influencing perceptions of safety, trust, and usability. While prior research has focused on developing techniques to emulate human preferences, and proposed taxonomies to categorize risks (such as psychological harms and algorithmic biases), these efforts have provided limited systematic understanding of lived human experiences or actionable strategies for embedding them meaningfully into the AI development lifecyc…

I’ve refreshed the “General Methodologies for Intelligence Analysis” section on threat-intelligence.eu
The page was a bit dated, so it now has cleaner references, better structure, and direct links to practical models (including MISP taxonomies).
🔗 https://threat-intelligence.eu/methodo…

Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
Ping Liu, Jianqiang Shen, Qianqi Shen, Chunnan Yao, Kevin Kao, Dan Xu, Rajat Arora, Baofen Zheng, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang
https://arxiv.org/abs/2509.09690

Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
Query understanding is essential in modern relevance systems, where user queries are often short, ambiguous, and highly context-dependent. Traditional approaches often rely on multiple task-specific Named Entity Recognition models to extract structured facets as seen in job search applications. However, this fragmented architecture is brittle, expensive to maintain, and slow to adapt to evolving taxonomies and language patterns. In this paper, we introduce a unified query understanding framewor…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi, Patricia Brassard, Hakim Khellaf, Vincent Quoc-Huy Trinh, Jennifer Zhang, Lina Chen, Corwyn Rowsell, Sonal Varma, Kostas Plataniotis, Mahdi S. Hosseini
https://ar…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to mul…

Since at this point everyone surely has seen
The Big OOPs: Anatomy of a Thirty-five-year Mistake
(and quartz, of course)
At its 2.5h & focus on game dev, it’s a bit difficult to generally recommend, but it's of course catnip to ME.
And this slide everyone should get framed.
We are not biologists. We don’t need to build taxonomies on what something IS. Focus on DOING, not BEING.
Almost every OOP intro gets this wrong.

A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora
Alessia Pisu, Livio Pompianu, Francesco Osborne, Diego Reforgiato Recupero, Daniele Riboni, Angelo Salatino
https://arxiv.org/abs/2508.04213

A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora
Taxonomies and ontologies of research topics (e.g., MeSH, UMLS, CSO, NLM) play a central role in providing the primary framework through which intelligent systems can explore and interpret the literature. However, these resources have traditionally been manually curated, a process that is time-consuming, prone to obsolescence, and limited in granularity. This paper presents Sci-OG, a semi-auto\-mated methodology for generating research topic ontologies, employing a multi-step approach: 1) Topic…

Systematic Evaluation of Multi-modal Approaches to Complex Player Profile Classification
Jason Starace, Terence Soule
https://arxiv.org/abs/2509.05624 https://

Systematic Evaluation of Multi-modal Approaches to Complex Player Profile Classification
Modern adaptive games require nuanced player understanding, yet most models use simplified 5-10 category taxonomies that fail to capture diversity. Behavioral clustering cannot distinguish players with different motivations who act similarly. We present a systematic evaluation of multi-modal classification at scale, combining behavioral telemetry with semantic context to support 36 player profiles. Using 19,413 gameplay sessions from an AI-controlled text-based RPG, we compared behavioral-only …

Detecting Struggling Student Programmers using Proficiency Taxonomies
Noga Schwartz, Roy Fairstein, Avi Segal, Kobi Gal
https://arxiv.org/abs/2508.17353 https://

Detecting Struggling Student Programmers using Proficiency Taxonomies
Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students' programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model…

Taxonomy of Faults in Attention-Based Neural Networks
Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma, Mohammad Masudur Rahman
https://arxiv.org/abs/2508.04925 https://

Taxonomy of Faults in Attention-Based Neural Networks
Attention mechanisms are at the core of modern neural architectures, powering systems ranging from ChatGPT to autonomous vehicles and driving a major economic impact. However, high-profile failures, such as ChatGPT's nonsensical outputs or Google's suspension of Gemini's image generation due to attention weight errors, highlight a critical gap: existing deep learning fault taxonomies might not adequately capture the unique failures introduced by attention mechanisms. This gap leaves practitione…

DMER-Ranker: Learning to Rank Emotion Descriptions in the Absence of Ground Truth
Zheng Lian, Licai Sun, Haoyu Chen, Zebang Cheng, Fan Zhang, Ziyu Jia, Ziyang Ma, Fei Ma, Xiaojiang Peng, Jianhua Tao
https://arxiv.org/abs/2507.04278

DMER-Ranker: Learning to Rank Emotion Descriptions in the Absence of Ground Truth
Descriptive Multimodal Emotion Recognition (DMER) is a newly proposed task that aims to describe a person's emotional state using free-form natural language. Unlike traditional discriminative methods that rely on predefined emotion taxonomies, DMER provides greater flexibility in emotional expression, enabling fine-grained and interpretable emotion representations. However, this free-form prediction paradigm introduces significant challenges in evaluation. Existing methods either depend on grou…

PolyGuard: Massive Multi-Domain Safety Policy-Grounded Guardrail Dataset
Mintong Kang, Zhaorun Chen, Chejian Xu, Jiawei Zhang, Chengquan Guo, Minzhou Pan, Ivan Revilla, Yu Sun, Bo Li
https://arxiv.org/abs/2506.19054

PolyGuard: Massive Multi-Domain Safety Policy-Grounded Guardrail Dataset
As LLMs become widespread across diverse applications, concerns about the security and safety of LLM interactions have intensified. Numerous guardrail models and benchmarks have been developed to ensure LLM content safety. However, existing guardrail benchmarks are often built upon ad hoc risk taxonomies that lack a principled grounding in standardized safety policies, limiting their alignment with real-world operational requirements. Moreover, they tend to overlook domain-specific risks, while…

A visual analytics tool for taxonomy-based trajectory data exploration
Ivan A. Hanono Cozzetti, Ahmad Abdou
https://arxiv.org/abs/2507.22899 https://arxiv.…

A visual analytics tool for taxonomy-based trajectory data exploration
The analysis of spatio-temporal data presents significant challenges due to the complexity and heterogeneity of movement patterns. This project proposes a data analytics tool that combines data visualization and statistical computation to facilitate spatio-temporal data analysis through a multi-level approach. The tool categorizes moving objects into distinct taxonomies using Machine Learning models, adding meaningful structure to the analysis. Two case studies demonstrate the methodology's eff…

PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation
Bin Tan, Wangyao Ge, Yidi Wang, Xin Liu, Jeff Burtoft, Hao Fan, Hui Wang
https://arxiv.org/abs/2508.18166

PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation
Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization. We propose PCR-CA (Parallel Codebook Representations with Contrastive Alignment), an end-to-end framework for improved CTR prediction. PCR-CA first extracts compact multimodal embeddings from app text, then introduces a Parallel Codebook VQ-AE module that learns discrete semantic representations across multiple codeboo…

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark D\'iaz, Charvi Rastogi, Aida Davani, Vinodkumar Prabhakaran, Pushkar Mishra, Roma Patel, Alicia Parrish, Zoe Ashwood, Michela Paganini, Tian Huey Teh, Verena Rieser, Lora Aroyo
https://

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends …