Tootfinder

AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Chathura Wimalasiri, Piumal Rathnayake, Shamod Wijerathne, Sumudu Rasnayaka, Dhanushka Leuke Bandara, Roshan Ragel, Vajira Thambawita, Isuru Nawinne
https://arxiv.org/abs/2506.20522

AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Periodontitis, a chronic inflammatory disease causing alveolar bone loss, significantly affects oral health and quality of life. Accurate assessment of bone loss severity and pattern is critical for diagnosis and treatment planning. In this study, we propose a novel AI-based deep learning framework to automatically detect and quantify alveolar bone loss and its patterns using intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth detection with Keypoint R-CNN models to id…

Decision-Focused Learning for Neural Network-Constrained Optimization: Application to HVAC Management System
Pietro Favaro, Jean-Fran\c{c}ois Toubeau, Fran\c{c}ois Vall\'ee, Yury Dvorkin
https://arxiv.org/abs/2506.19717

Decision-Focused Learning for Neural Network-Constrained Optimization: Application to HVAC Management System
Heating, Ventilation, and Air Conditioning (HVAC) is a major electricity end-use with a substantial potential for grid services such as demand response. Harnessing this flexibility requires accurate modeling of the thermal dynamics of buildings, which is challenging due to their nonlinear and repetitive behavior (e.g., daily pattern), which reduce the value of historical data. To address this issue, this paper presents an HVAC management system formulated as a Mixed Integer Quadratic Program (M…

C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning
Shusen Ma, Yun-Bo Zhao, Yu Kang
https://arxiv.org/abs/2507.17454

C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning
Multivariate time series forecasting has drawn increasing attention due to its practical importance. Existing approaches typically adopt either channel-mixing (CM) or channel-independence (CI) strategies. CM strategy can capture inter-variable dependencies but fails to discern variable-specific temporal patterns. CI strategy improves this aspect but fails to fully exploit cross-variable dependencies like CM. Hybrid strategies based on feature fusion offer limited generalization and interpretabi…

PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
Haotong Du, Yaqing Wang, Fei Xiong, Lei Shao, Ming Liu, Hao Gu, Quanming Yao, Zhen Wang
https://arxiv.org/abs/2506.18382

PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
With the expansion of business scales and scopes on online platforms, multi-scenario matching has become a mainstream solution to reduce maintenance costs and alleviate data sparsity. The key to effective multi-scenario recommendation lies in capturing both user preferences shared across all scenarios and scenario-aware preferences specific to each scenario. However, existing methods often overlook user-specific modeling, limiting the generation of personalized user representations. To address …

Adaptive alert prioritisation in security operations centres via learning to defer with human feedback
Fatemeh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, C\'ecile Paris
https://arxiv.org/abs/2506.18462

Adaptive alert prioritisation in security operations centres via learning to defer with human feedback
Alert prioritisation (AP) is crucial for security operations centres (SOCs) to manage the overwhelming volume of alerts and ensure timely detection and response to genuine threats, while minimising alert fatigue. Although predictive AI can process large alert volumes and identify known patterns, it struggles with novel and evolving scenarios that demand contextual understanding and nuanced judgement. A promising solution is Human-AI teaming (HAT), which combines human expertise with AI's comput…

A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
Xiaojie Li, Zhijie Cai, Nan Qi, Chao Dong, Guangxu Zhu, Haixia Ma, Qihui Wu, Shi Jin
https://arxiv.org/abs/2507.14186

A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-a…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- A Survey of Deep Learning for Geometry Problem Solving
Jianzhe Ma, Wenxuan Wang, Qin Jin

An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
Shivam Verma, Vivian Chen, Darren Mei
https://arxiv.org/abs/2506.18735

An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
Spotify, a large-scale multimedia platform, attracts over 675 million monthly active users who collectively consume millions of hours of music, podcasts, audiobooks, and video content. This diverse content consumption pattern introduces unique challenges for computational advertising, which must effectively integrate a variety of ad modalities, including audio, video, and display, within a single user experience. Traditional ad recommendation models, primarily designed for foregrounded experien…

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra
https://arxiv.org/abs/2506.16224

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, featu…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning
Bob Zhang, Haoran Li, Tao Zhang, Cilin Yan, Jiayin Cai, Yanbin Hao