Tootfinder

A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
Matej Gjurkovi\'c
https://arxiv.org/abs/2510.02811 https://

A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
Personality refers to individual differences in behavior, thinking, and feeling. With the growing availability of digital footprints, especially from social media, automated methods for personality assessment have become increasingly important. Natural language processing (NLP) enables the analysis of unstructured text data to identify personality indicators. However, two main challenges remain central to this thesis: the scarcity of large, personality-labeled datasets and the disconnect betwee…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

Engineering Automotive Digital Twins on Standardized Architectures: A Case Study
Stefan Ramdhan, Winnie Trandinh, Istvan David, Vera Pantelic, Mark Lawford
https://arxiv.org/abs/2508.18662

Engineering Automotive Digital Twins on Standardized Architectures: A Case Study
Digital twin (DT) technology has become of interest in the automotive industry. There is a growing need for smarter services that utilize the unique capabilities of DTs, ranging from computer-aided remote control to cloud-based fleet coordination. Developing such services starts with the software architecture. However, the scarcity of DT architectural guidelines poses a challenge for engineering automotive DTs. Currently, the only DT architectural standard is the one defined in ISO 23247. Thoug…

Controllable Latent Space Augmentation for Digital Pathology
Sofi\`ene Boutaj, Marin Scalbert, Pierre Marza, Florent Couzinie-Devy, Maria Vakalopoulou, Stergios Christodoulidis
https://arxiv.org/abs/2508.14588

Controllable Latent Space Augmentation for Digital Pathology
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multiple Instance Learning (MIL) is a natural fit for slide-level tasks, training robust models requires large and diverse datasets. Even though image augmentation techniques could be utilized to increase data variability and reduce overfitting, implementing them effectively is not a trivial task. Traditional patch-level au…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion
Georges V. Houngbonon, Marc Ivaldi, Emil Palikot, Davide Strusani
https://arxiv.org/abs/2507.19693

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion
Nearly half the world remains offline, and capital scarcity stalls new network buildouts. Sharing existing mobile towers could accelerate connectivity. We assemble data on 107 tower-sharing deals in 28 low-income countries (2008-20) and estimate staggered difference-in-differences effects. Two years after a transaction covering over 1,000 towers, the PPP-adjusted mobile-price index falls USD 1.60 (s.e. 1.10) from a baseline of USD 3.16, while data prices drop USD 1.00 (0.29), baseline USD 3.41 …

Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
Jiamu Wang, Keunho Byeon, Jinsol Song, Anh Nguyen, Sangjeong Ahn, Sung Hak Lee, Jin Tae Kwak
https://arxiv.org/abs/2508.15236

Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approaches deliver high accuracy, they rely on extensively annotated datasets, suffering from data scarcity in digital pathology. Unsupervised anomaly detection, however, offers a viable alternative by identifying deviations from normal tissue distributions without requiring exhaustive annotations. Recently, denoising diffusi…

SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
Jinyang Wu, Nana Hou, Zihan Pan, Qiquan Zhang, Sailor Hardik Bhupendra, Soumik Mondal
https://arxiv.org/abs/2509.19865

SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes, yet current datasets cover SEA languages only sparsely, leaving models poorly equipped to handle this critical region. This omission is critical: detection models trained on high-resource languages collapse when applied to SEA, due to mismatches in synthesis quality, language-specific characteristics, and data scarcity. To close this gap, we present SEA-Spoof, the first large-scale Audio…

How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
Clement Ruah, Houssem Sifaou, Osvaldo Simeone, Bashir M. Al-Hashimi
https://arxiv.org/abs/2507.07067

How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datase…

A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
Jiale Zhang, Pengfei He, Fei Li, Kewei Li, Yan Wang, Lan Huang, Ruochi Zhang, Fengfeng Zhou
https://arxiv.org/abs/2509.04925

A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
In today's fast-paced digital communication, the surge in network traffic data and frequency demands robust and precise network intrusion solutions. Conventional machine learning methods struggle to grapple with complex patterns within the vast network intrusion datasets, which suffer from data scarcity and class imbalance. As a result, we have integrated machine learning and deep learning techniques within the network intrusion detection system to bridge this gap. This study has developed Trai…

Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification
Xiang Tuo, Xu Xuemiao, Liu Bangzhen, Li Jinyi, Li Yong, He Shengfeng
https://arxiv.org/abs/2509.14958

Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification
The rapid growth of 3D digital content necessitates expandable recognition systems for open-world scenarios. However, existing 3D class-incremental learning methods struggle under extreme data scarcity due to geometric misalignment and texture bias. While recent approaches integrate 3D data with 2D foundation models (e.g., CLIP), they suffer from semantic blurring caused by texture-biased projections and indiscriminate fusion of geometric-textural cues, leading to unstable decision prototypes a…

Can maiBERT Speak for Maithili?
Sumit Yadav, Raju Kumar Yadav, Utsav Maskey, Gautam Siddharth Kashyap Md Azizul Hoque, Ganesh Gautam
https://arxiv.org/abs/2509.15048 https://

Can maiBERT Speak for Maithili?
Natural Language Understanding (NLU) for low-resource languages remains a major challenge in NLP due to the scarcity of high-quality data and language-specific models. Maithili, despite being spoken by millions, lacks adequate computational resources, limiting its inclusion in digital and AI-driven applications. To address this gap, we introducemaiBERT, a BERT-based language model pre-trained specifically for Maithili using the Masked Language Modeling (MLM) technique. Our model is trained on a…