Tootfinder

Arabic Hate Speech Identification and Masking in Social Media using Deep Learning Models and Pre-trained Models Fine-tuning
Salam Thabet Doghmash, Motaz Saad
https://arxiv.org/abs/2507.23661

Arabic Hate Speech Identification and Masking in Social Media using Deep Learning Models and Pre-trained Models Fine-tuning
Hate speech identification in social media has become an increasingly important issue in recent years. In this research, we address two problems: 1) to detect hate speech in Arabic text, 2) to clean a given text from hate speech. The meaning of cleaning here is replacing each bad word with stars based on the number of letters for each word. Regarding the first problem, we conduct several experiments using deep learning models and transformers to determine the best model in terms of the F1 score…

Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
Rapha\"el Bourgade, Guillaume Balezo, Thomas Walter
https://arxiv.org/abs/2509.02593 https://a…

Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
Mitotic figures represent a key histoprognostic feature in tumor pathology, providing crucial insights into tumor aggressiveness and proliferation. However, their identification remains challenging, subject to significant inter-observer variability, even among experienced pathologists. To address this issue, the MItosis DOmain Generalization (MIDOG) 2025 challenge marks the third edition of an international competition aiming to develop robust mitosis detection algorithms. In this paper, we pre…

Nonparametric Bounds in Causal Mediation Analysis in the Presence of Unmeasured Confounding and Imperfect Compliance
Wei Liang, Changbao Wu
https://arxiv.org/abs/2509.02756 http…

Nonparametric Bounds in Causal Mediation Analysis in the Presence of Unmeasured Confounding and Imperfect Compliance
The average causal mediation effect (ACME) and the natural direct effect (NDE) are two parameters of primary interest in causal mediation analysis. However, the two causal parameters are not identifiable from randomized experimental data in the presence of outcome-mediator confounding and treatment-assignment noncompliance. Sjölander (2009) addressed the partial identification issue and derived nonparametric bounds of the NDE in randomized controlled trials under a set of monotonicity assumpti…

FHSTP@EXIST 2025 Benchmark: Sexism Detection with Transparent Speech Concept Bottleneck Models
Roberto Labadie-Tamayo, Adrian Jaques B\"ock, Djordje Slijep\v{c}evi\'c, Xihui Chen, Andreas Babic, Matthias Zeppelzauer
https://arxiv.org/abs/2507.20924

FHSTP@EXIST 2025 Benchmark: Sexism Detection with Transparent Speech Concept Bottleneck Models
Sexism has become widespread on social media and in online conversation. To help address this issue, the fifth Sexism Identification in Social Networks (EXIST) challenge is initiated at CLEF 2025. Among this year's international benchmarks, we concentrate on solving the first task aiming to identify and classify sexism in social media textual posts. In this paper, we describe our solutions and report results for three subtasks: Subtask 1.1 - Sexism Identification in Tweets, Subtask 1.2 - Source…

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Zhanhong Jiang, Dylan Shah, Hsin-Jung Yang, Soumik Sarkar
https://arxiv.org/abs/2507.07370

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To tackle the issue, numerous data-driven machine learning approaches have been proposed for modeling nonlinear dynamics. However, these models suffer from prediction uncertainty that can negatively affect modeling accuracy, and uncertainty quantification for kinematic modeling in soft robots is underexplored. In this w…

IDFace: Face Template Protection for Efficient and Secure Identification
Sunpill Kim, Seunghun Paik, Chanwoo Hwang, Dongsoo Kim, Junbum Shin, Jae Hong Seo
https://arxiv.org/abs/2507.12050

IDFace: Face Template Protection for Efficient and Secure Identification
As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such as homomorphic encryption (HE) have provided opportunities for securing the FRS, HE cannot be used directly with FRS in an efficient plug-and-play manner. In particular, although HE is functionally c…

Staggered Adoption DiD Designs with Misclassification and Anticipation
Clara Augustin, Daniel Gutknecht, Cenchen Liu
https://arxiv.org/abs/2507.20415 https://

Staggered Adoption DiD Designs with Misclassification and Anticipation
This paper examines the identification and estimation of treatment effects in staggered adoption designs -- a common extension of the canonical Difference-in-Differences (DiD) model to multiple groups and time-periods -- in the presence of (time varying) misclassification of the treatment status as well as of anticipation. We demonstrate that standard estimators are biased with respect to commonly used causal parameters of interest under such forms of misspecification. To address this issue, we…

Question for any folk that do reader/audience/play testing.
I find that the more fidelity (detail, production value) a project has, the less accurate the user's identification of an issue. I try to interpret accordingly.
Do you know of any studies regarding this or personal experiences (that confirm or refute this)? I have studies on how people don't know what they want, etc. But keen on the connection btw fidelity & inaccuracy
Thank you! :)
Other relate…

From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
Rom Gutman, Shimon Sheiba, Omer Noy Klien, Naama Dekel Bird, Amit Gruber, Doron Aronson, Oren Caspi, Uri Shalit
https://arxiv.org/abs/2507.11381

From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus on safety and validity, including the crucial issue of causal identification when using observational data. We do not provide a specific model, but rather a way to integrate existing methods and know-how into a practical pipeline. We further provide a real wor…

Pothole Detection and Recognition based on Transfer Learning
Mang Hu, Qianqian Xia
https://arxiv.org/abs/2509.06750 https://arxiv.org/pdf/2509.06750…

Pothole Detection and Recognition based on Transfer Learning
With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social development to conduct an in-depth analysis of road images through feature extraction, thereby achieving automatic identification of the pothole condition in new images. Consequently, this is the main issue addressed in this study. Based on preprocessing techniques suc…

ICR: Iterative Clarification and Rewriting for Conversational Search
Zhiyu Cao, Peifeng Li, Qiaoming Zhu
https://arxiv.org/abs/2509.05100 https://arxiv.org…

ICR: Iterative Clarification and Rewriting for Conversational Search
Most previous work on Conversational Query Rewriting employs an end-to-end rewriting paradigm. However, this approach is hindered by the issue of multiple fuzzy expressions within the query, which complicates the simultaneous identification and rewriting of multiple positions. To address this issue, we propose a novel framework ICR (Iterative Clarification and Rewriting), an iterative rewriting scheme that pivots on clarification questions. Within this framework, the model alternates between ge…

Extension of Spatial k-Anonymity: New Metrics for Assessing the Anonymity of Geomasked Data Considering Realistic Attack Scenarios
Simon Cremer, Lydia Jehmlich, Rainer Lenz
https://arxiv.org/abs/2509.07505

Extension of Spatial k-Anonymity: New Metrics for Assessing the Anonymity of Geomasked Data Considering Realistic Attack Scenarios
Spatial data are gaining increasing importance in many areas of research. Particularly spatial health data are becoming increasingly important for medical research, for example, to better understand relationships between environmental factors and disease patterns. However, their use is often restricted by legal data protection regulations, since georeferenced personal information carries a high risk of re-identification of individuals. To address this issue, what are called geomasking methods a…