Tootfinder

CKM-Assisted Physical-Layer Security for Resilience Against Unknown Eavesdropping Location
Ladan Khaloopour, Matthias Hollick, Vahid Jamali
https://arxiv.org/abs/2508.13681 http…

CKM-Assisted Physical-Layer Security for Resilience Against Unknown Eavesdropping Location
Channel Knowledge Map (CKM) is an emerging data-driven toolbox that captures our awareness of the wireless channel and enables efficient communication and resource allocation beyond the state of the art. In this work, we consider CKM for improving physical-layer security (PLS) in the presence of a passive eavesdropper (Eve), without making any assumptions about Eve's location or channel state information (CSI). We employ highly directional mmWave transmissions, with the confidential message joi…

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
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University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
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Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Lucien Heitz, Runze Li, Oana Inel, Abraham Bernstein
https://arxiv.org/abs/2508.13019

Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Norm-aware recommender systems have gained increased attention, especially for diversity optimization. The recommender systems community has well-established experimentation pipelines that support reproducible evaluations by facilitating models' benchmarking and comparisons against state-of-the-art methods. However, to the best of our knowledge, there is currently no reproducibility framework to support thorough norm-driven experimentation at the pre-processing, in-processing, post-processing, …

"Is #OpenAccess for Everyone?" https://katinamagazine.org/content/article/open-knowledge/2025/is-open-access-for-everyone

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
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University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
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More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generation
Yangtian Zi, Harshitha Menon, Arjun Guha
https://arxiv.org/abs/2508.03678 https://

More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generation
State-of-the-art Large Language Models (LLMs) achieve high pass@1 on general benchmarks like HumanEval but underperform on specialized suites such as ParEval. Is this due to LLMs missing domain knowledge or insufficient prompt detail is given? To answer this, we introduce PartialOrderEval, which augments any code generation benchmark with a partial order of prompts from minimal to maximally detailed. Applying it to HumanEval and both serial and OpenMP subsets of ParEval, we measure how pass@1 s…

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels
Lucas Rakotoarivony
https://arxiv.org/abs/2508.21424 https://arxiv.org/pdf/2508.2142…

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels
Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that were unseen during training often emerge, requiring models to acquire new knowledge incrementally. Class-Incremental Learning (CIL) methods enable a model to learn novel classes while retaining knowledge of previous classes. However, these methods make the strong assumption that the incremental dataset is fully labeled, which is unrealistic in pract…

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
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University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
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SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2
Guoping Xu, Yan Dai, Hengrui Zhao, Ying Zhang, Jie Deng, Weiguo Lu, You Zhang
https://arxiv.org/abs/2507.19282

SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2
Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for prompt-based segmentation but struggles with tumor accuracy. We propose prior knowledge-based augmentation strategies to enhance SAM2 for ART. Methods: Two strategies were introduced to improve SAM2: (1) using prior MR images and annotations as contextual inputs, and (2) improving prompt robustness via random boundi…

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Lillian Asiala, James E. McCarthy
https://arxiv.org/abs/2507.18878

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Sonalysts, Inc. (Sonalysts) is working on an initiative to expand our expertise in teaming to include Human-Artificial Intelligence (AI) teams. The first step of this process is to develop a Synthetic Task Environment (STE) to support our original research. Prior knowledge elicitation efforts within the Human-AI teaming research stakeholder community revealed a desire to support data collection using pre- and post-performance surveys. In this technical report, we review a number of constructs t…

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
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University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
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Relational Database Distillation: From Structured Tables to Condensed Graph Data
Xinyi Gao, Jingxi Zhang, Lijian Chen, Tong Chen, Lizhen Cui, Hongzhi Yin
https://arxiv.org/abs/2510.06980

Relational Database Distillation: From Structured Tables to Condensed Graph Data
Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the knowledge within RDBs for predictive tasks, recent advances leverage graph representation learning to capture complex inter-table relations as multi-hop dependencies. Despite achieving state-of-the-art performance, these methods remain hindered by the prohibitive storage overhead and excessive training time, due to the m…

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
N. Jean, G. Le Pera
https://arxiv.org/abs/2509.02388 https://arxiv.org…

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is often unmet in practice. This paper proposes a general purpose framework that bridges state of the art explainability techniques with Malle's five category model of behavior explanation: Knowledge Structures, Simulation/Projection, Covariation, Direct Recall, and Rationalization. The framework is designed to be applicabl…

NASA Exoplanet Exploration Program (ExEP) Science Gap List
Karl Stapelfeldt, Eric Mamajek
https://arxiv.org/abs/2507.18665 https://arxiv.org/pdf/2507.18665…

NASA Exoplanet Exploration Program (ExEP) Science Gap List
The Exoplanet Exploration Program (ExEP) is chartered by the NASA Astrophysics Division to carry out science, research, and technology tasks that advance NASA's science goals for exoplanets. The ExEP Science Gap List is a compilation of "science gaps", defined as either: 1) The difference between knowledge needed to define requirements for specified future NASA exoplanet missions and the current state of the art, or 2) Knowledge which is needed to enhance the exoplanet science return of current…

TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task
Michael R. Bock, Kara Molisee, Zachary Ozer, Sumit Shah
https://arxiv.org/abs/2507.16126

TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task
Can AI file your taxes? Not yet. Calculating US personal income taxes is a task that requires building an understanding of vast amounts of English text and using that knowledge to carefully compute results. We propose TaxCalcBench, a benchmark for determining models' abilities to calculate personal income tax returns given all of the necessary information. Our experiment shows that state-of-the-art models succeed in calculating less than a third of federal income tax returns even on this simpli…

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
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University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
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SlotMatch: Distilling Temporally Consistent Object-Centric Representations for Unsupervised Video Segmentation
Diana-Nicoleta Grigore, Neelu Madan, Andreas Mogelmose, Thomas B. Moeslund, Radu Tudor Ionescu
https://arxiv.org/abs/2508.03411

SlotMatch: Distilling Temporally Consistent Object-Centric Representations for Unsupervised Video Segmentation
Unsupervised video segmentation is a challenging computer vision task, especially due to the lack of supervisory signals coupled with the complexity of visual scenes. To overcome this challenge, state-of-the-art models based on slot attention often have to rely on large and computationally expensive neural architectures. To this end, we propose a simple knowledge distillation framework that effectively transfers object-centric representations to a lightweight student. The proposed framework, ca…

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
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University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
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Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK) https://networks.h-net.org/group/announcements/20122004/2025-canadian-friends-sufi-arts-culture-and-knowledge-cfsack-research…

VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes
Paul Gavrikov, Wei Lin, M. Jehanzeb Mirza, Soumya Jahagirdar, Muhammad Huzaifa, Sivan Doveh, Serena Yeung-Levy, James Glass, Hilde Kuehne
https://arxiv.org/abs/2509.25339

VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes
Is basic visual understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question-answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free vision tasks in densely populated (or, overloaded) scenes. Our dataset consists of high-resolution scans …

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
https://ift.tt/LrB1sdG
University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
via Input 4 RELCFP

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
https://ift.tt/c5np7h8
University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
via Input 4 RELCFP

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
https://ift.tt/0faCeTQ
University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
via Input 4 RELCFP

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
https://ift.tt/s0Wro5b
Rutgers Global Affairs Conference now VIRTUAL - new CFP deadline Rutgers University…
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Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
https://ift.tt/IUyLVFs
Rutgers Global Affairs Conference now VIRTUAL - new CFP deadline Rutgers University…
via Input 4 RELCFP

Research Awards - 2025 Canadian Friends of Sufi Arts, Culture, and Knowledge (CFSACK)
https://ift.tt/HmCjVTI
Rutgers Global Affairs Conference now VIRTUAL - new CFP deadline Rutgers University…
via Input 4 RELCFP