Tootfinder

Assessment Twins: A Protocol for AI-Vulnerable Summative Assessment
Jasper Roe (Durham University, UK), Mike Perkins (British University Vietnam), Louie Giray (Mapua University, Philippines)
https://arxiv.org/abs/2510.02929

Assessment Twins: A Protocol for AI-Vulnerable Summative Assessment
Generative Artificial Intelligence (GenAI) is reshaping higher education and raising pressing concerns about the integrity and validity of higher education assessment. While assessment redesign is increasingly seen as a necessity, there is a relative lack of literature detailing what such redesign may entail. In this paper, we introduce assessment twins as an accessible approach for redesigning assessment tasks to enhance validity. We use Messick's unified validity framework to systematically m…

ELMF4EggQ: Ensemble Learning with Multimodal Feature Fusion for Non-Destructive Egg Quality Assessment
Md Zahim Hassan, Md. Osama, Muhammad Ashad Kabir, Md. Saiful Islam, Zannatul Naim
https://arxiv.org/abs/2510.02876

ELMF4EggQ: Ensemble Learning with Multimodal Feature Fusion for Non-Destructive Egg Quality Assessment
Accurate, non-destructive assessment of egg quality is critical for ensuring food safety, maintaining product standards, and operational efficiency in commercial poultry production. This paper introduces ELMF4EggQ, an ensemble learning framework that employs multimodal feature fusion to classify egg grade and freshness using only external attributes - image, shape, and weight. A novel, publicly available dataset of 186 brown-shelled eggs was constructed, with egg grade and freshness levels dete…

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…

New Gist: Presidential Numberwang
On a pure horserace assessment of today's Sunday Independent poll, Jim Gavin seems destined for the glue factory. But picking the Copydex candidate may prove fatal for Michešl Martin.
https://www.thegist.ie/the-gist-presidential-numberwan…

The Gist: Presidential Numberwang
After weeks of vibereporting, the first big poll of the campaign proper is out. This is the Gist.

The #glyphosate study published in 2000, on which probably most glyphosate regulations are based, has been retracted.
1️⃣ There are indications that #Monsanto (the manufacturer of #Roundup,…

"depraved fiend": Stephen Miller hit with another scathing assessment from his own uncle - Raw Story
https://www.rawstory.com/stephen-miller-2674160123/

Meta is in the scam business. According to an internal assessment, 10% of its mind-blowing revenue is generated by scam. As a consequence, Meta actually promotes such content, charging it a higher rate, until a high-profile authority draws attention to it.
https://

Bombshell report exposes how Meta relied on scam ad profits to fund AI
Meta goosed its revenue by targeting users likely to click on scam ads, docs show.

Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights
Daphne Tsolissou, Theofanis Ganitidis, Konstantinos Mitsis, Stergios CHristodoulidis, Maria Vakalopoulou, Konstantina Nikita
https://arxiv.org/abs/2510.02922

Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights
Reliable risk assessment for carotid atheromatous disease remains a major clinical challenge, as it requires integrating diverse clinical and imaging information in a manner that is transparent and interpretable to clinicians. This study investigates the potential of state-of-the-art and recent large vision-language models (LVLMs) for multimodal carotid plaque assessment by integrating ultrasound imaging (USI) with structured clinical, demographic, laboratory, and protein biomarker data. A fram…

SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment
Yuxun Tang, Lan Liu, Wenhao Feng, Yiwen Zhao, Jionghao Han, Yifeng Yu, Jiatong Shi, Qin Jin
https://arxiv.org/abs/2510.01812

SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment
Singing voice generation progresses rapidly, yet evaluating singing quality remains a critical challenge. Human subjective assessment, typically in the form of listening tests, is costly and time consuming, while existing objective metrics capture only limited perceptual aspects. In this work, we introduce SingMOS-Pro, a dataset for automatic singing quality assessment. Building on our preview version SingMOS, which provides only overall ratings, SingMOS-Pro expands annotations of the additiona…

POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment
Luoxi Tang, Yuqiao Meng, Ankita Patra, Weicheng Ma, Muchao Ye, Zhaohan Xi
https://arxiv.org/abs/2510.01552

POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence (CTI) to support vulnerability assessment and incident response. While recent work has shown that LLMs can support a wide range of CTI tasks such as threat analysis, vulnerability detection, and intrusion defense, significant performance gaps persist in practical deployments. In this paper, we investigate the intrins…

The frontline in Ukraine is not facing imminent collapse despite recent Russian gains and Kremlin assertions.
https://understandingwar.org/research/russia-ukraine/russian-offensive-campaign-assessment-november-28-2025/

Russian Offensive Campaign Assessment, November 28, 2025
The frontline in Ukraine is not facing imminent collapse despite recent Russian gains and Kremlin assertions.

AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment
Yusif Ibrahimov, Tarique Anwar, Tommy Yuan, Turan Mutallimov, Elgun Hasanov
https://arxiv.org/abs/2510.00706

AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment
In today's interconnected society, social media platforms provide a window into individuals' thoughts, emotions, and mental states. This paper explores the use of platforms like Facebook, X (formerly Twitter), and Reddit for depression severity detection. We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation by fusing contextual and domain knowledge. Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms hi…

The FBI director, Kash Patel, is
“in over his head”
and leading a “chronically under-performing” agency
paralyzed by fear and plummeting morale,
according to a scathing 115-page report compiled by a national alliance of retired and active-duty FBI special agents and analysts.
The leaked assessment, obtained by the New York Post and prepared for both congressional Senate and House judiciary committees, is based on confidential accounts from 24 FBI sources.
…

Don't miss today's Metacurity for the most critical infosec developments you should know, including
--Chinese hackers breached diplomats' email servers in a years-long campaign,
--China sentences 11 people to death for gambling and Myanmar scam operations,
--DOJ settles with Georgia Tech over fake cyber assessment score,
--Google launches new ransomware protection,
--Microsoft launches a security store,
--BNB Chain X account hacked,
--Img…

Chinese hackers breached diplomats' email servers in a years-long campaign
China sentences 11 people to death for gambling and Myanmar scam operations, DOJ settles with Georgia Tech over fake cyber assessment score, Google launches new ransomware protection, Microsoft launches a security store, BNB Chain X account hacked, Imgur blocked in UK, much more

AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention
Suhyeon Lim, Ye-eun Kim, Andrew J. Choi
https://arxiv.org/abs/2510.00049 https://

AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention
Effective stroke recovery requires continuous rehabilitation integrated with daily living. To support this need, we propose a home-based rehabilitation exercise and feedback system. The system consists of (1) hardware setup with RGB-D camera and wearable sensors to capture Stroke movements, (2) a mobile application for exercise guidance, and (3) an AI server for assessment and feedback. When Stroke user exercises following the application guidance, the system records skeleton sequences, which a…

ESPN Has Troubling Assessment of Raiders QB Geno Smith https://heavy.com/sports/nfl/las-vegas-raiders/geno-smith-interceptions-espn/?adt_ei=[email]

Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Sophia V. Kuhn, Rafael Bischof, Marius Weber, Antoine Binggeli, Michael A. Kraus, Walter Kaufmann, Fernando P\'erez-Cruz
https://arxiv.org/abs/2509.25031

Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Aging infrastructure portfolios pose a critical resource allocation challenge: deciding which structures require intervention and which can safely remain in service. Structural assessments must balance the trade-off between cheaper, conservative analysis methods and accurate but costly simulations that do not scale portfolio-wide. We propose Bayesian neural network (BNN) surrogates for rapid structural pre-assessment of worldwide common bridge types, such as reinforced concrete frame bridges. T…

Elastic Energy Storage Mechanism in Hovering Animal Flight: A Discriminative Method Based on Wing Kinematics
Shijie Sheng, Jianghao Wu, Renxuan Bo, Long Chen, Yanlai Zhang
https://arxiv.org/abs/2510.02819

Elastic Energy Storage Mechanism in Hovering Animal Flight: A Discriminative Method Based on Wing Kinematics
Existing research has yet to reach a consensus on whether and how small flying animals utilize elastic energy storage mechanisms to reduce flight energy expenditure, and there is a lack of systematic and universal methods for assessment. To address these gaps, this study proposes a method to evaluate elastic energy storage capacity based on wing kinematic parameters (flapping amplitude and flapping frequency), grounded in the hypothesis that animals tend to minimize flight energy expenditure. B…

Evaluating Large Language Models for IUCN Red List Species Information
Shinya Uryu
https://arxiv.org/abs/2510.02830 https://arxiv.org/pdf/2510.02830…

Evaluating Large Language Models for IUCN Red List Species Information
Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for s…

Israeli startup Zafran, an AI-native threat exposure management service, raised a $60M Series C led by Menlo Ventures, bringing its total funding to $130M (Globes)
https://en.globes.co.il/en/article-israeli-startup-zafran-security-raises-60m-100152808…

Israeli startup Zafran Security raises $60m
Zafran has also launched its agentic exposure management suite, positioning it to complete the entire end-to-end lifecycle across asset inventory, vulnerability detection, risk assessment, and autonomous remediation workflows.

NL NCTV
25 SEP 2025
2025 Threat Assessment on State Actors (DBSA)
site: https://english.nctv.nl/documents/publications/2025/09/25/threat-assessment-of-state-actors-2025
pdf:

A Physics-Informed Multi-Source Domain Adaptation Framework for Label-Free Post-Earthquake Damage Assessment
Yifeng Zhang, Xiao Liang
https://arxiv.org/abs/2509.26356 https://…

A Physics-Informed Multi-Source Domain Adaptation Framework for Label-Free Post-Earthquake Damage Assessment
Efficient and intelligent assessment of post-earthquake structural damage is critical for rapid disaster response. While data-driven approaches have shown promise, traditional supervised learning methods rely on extensive labeled datasets, which are often impractical to obtain for damaged structures. To address this limitation, we propose a physics-informed multi-source domain adaptation framework to predict post-earthquake structural damage for a target building without requiring damage labels…

Doctoral Researcher opening: Assessment of changing trace metal fluxes in the Central and Eastern Arctic Ocean - Technische Universität Braunschweig #geology #geochemistry

Assessment of the $a_{2}(1320)$ tensor-meson contribution to $\eta/\eta^{\prime}\to\pi^{0}\gamma\gamma$ decays
Rafel Escribano, Sergi Gonz\`alez-Sol\'is, Emilio Royo
https://arxiv.org/abs/2510.00787

Assessment of the $a_{2}(1320)$ tensor-meson contribution to $η/η^{\prime}\toπ^{0}γγ$ decays
In light of the recent measurement of the $η\toπ^{0}γγ$ decay by the KLOE-2 Collaboration, a previous analysis including vector- and scalar-meson exchange contributions using the VMD and L$σ$M frameworks, respectively, is extended in the present study to incorporate the effects of the $a_{2}(1320)$ tensor meson within a chiral context. Although the individual contribution of the $a_{2}$ is negligible, its destructive interference with the vector-meson resonances is found to be significant,…

MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs
Jiyao Liu, Jinjie Wei, Wanying Qu, Chenglong Ma, Junzhi Ning, Yunheng Li, Ying Chen, Xinzhe Luo, Pengcheng Chen, Xin Gao, Ming Hu, Huihui Xu, Xin Wang, Shujian Gao, Dingkang Yang, Zhongying Deng, Jin Ye, Lihao Liu, Junjun He, Ningsheng Xu
https://arxiv…

MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs
Medical Image Quality Assessment (IQA) serves as the first-mile safety gate for clinical AI, yet existing approaches remain constrained by scalar, score-based metrics and fail to reflect the descriptive, human-like reasoning process central to expert evaluation. To address this gap, we introduce MedQ-Bench, a comprehensive benchmark that establishes a perception-reasoning paradigm for language-based evaluation of medical image quality with Multi-modal Large Language Models (MLLMs). MedQ-Bench d…

"First assessment of UNESCO sites finds widespread climate impacts"
#UNESCO #Climate #ClimateChange

First assessment of UNESCO sites finds widespread climate impacts
Nearly all of UNESCO’s more than 2,200 World Heritage Sites, Biosphere Reserves and Geoparks have already endured climate extremes over the past two decades. That’s according to initial data from the first ever global biodiversity and climate assessment unveiled at the UNESCO World Congress of Biosphere Reserves, held in Hangzhou, China, from Sept. 22-26. The […]

Grid Frequency Stability Support Potential of Data Center: A Quantitative Assessment of Flexibility
Pengyu Ren, Wei Sun, Yifan Wang, Gareth Harrison
https://arxiv.org/abs/2510.01050

Grid Frequency Stability Support Potential of Data Center: A Quantitative Assessment of Flexibility
The rapid expansion of data center infrastructure is reshaping power system dynamics by significantly increasing electricity demand while also offering potential for fast and controllable flexibility. To ensure reliable operation under such conditions, the frequency secured unit commitment problem must be solved with enhanced modeling of demand side frequency response. In this work, we propose a data-driven linearization framework based on decision tree based constraint learning to embed nonlin…

The negotiation process is not standing still! Trump gave a positive assessment #shorts: https://benborges.xyz/2025/12/01/the-negotiation-process-is-not.html

Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
Shoumik Saha, Jifan Chen, Sam Mayers, Sanjay Krishna Gouda, Zijian Wang, Varun Kumar
https://arxiv.org/abs/2510.01359

Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
Code-capable large language model (LLM) agents are increasingly embedded into software engineering workflows where they can read, write, and execute code, raising the stakes of safety-bypass ("jailbreak") attacks beyond text-only settings. Prior evaluations emphasize refusal or harmful-text detection, leaving open whether agents actually compile and run malicious programs. We present JAWS-BENCH (Jailbreaks Across WorkSpaces), a benchmark spanning three escalating workspace regimes that mirror a…

Baltimore Ravens cooked at 1-3? What's wrong with Sauce Gardner? Kansas City Chiefs truly back? https://www.nfl.com/news/baltimore-ravens-cooked-at-1-3-what-s-wrong-with-sauce-gardner-kansas-city-chiefs-truly-back…

The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
Alessio Buscemi, Thibault Simonetto, Daniele Pagani, German Castignani, Maxime Cordy, Jordi Cabot
https://arxiv.org/abs/2509.25256

The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
The systematic assessment of AI systems is increasingly vital as these technologies enter high-stakes domains. To address this, the EU's Artificial Intelligence Act introduces AI Regulatory Sandboxes (AIRS): supervised environments where AI systems can be tested under the oversight of Competent Authorities (CAs), balancing innovation with compliance, particularly for startups and SMEs. Yet significant challenges remain: assessment methods are fragmented, tests lack standardisation, and feedback…

Similarity-Based Assessment of Computational Reproducibility in Jupyter Notebooks
A S M Shahadat Hossain, Colin Brown, David Koop, Tanu Malik
https://arxiv.org/abs/2509.23645 ht…

Similarity-Based Assessment of Computational Reproducibility in Jupyter Notebooks
Computational reproducibility refers to obtaining consistent results when rerunning an experiment. Jupyter Notebook, a web-based computational notebook application, facilitates running, publishing, and sharing computational experiments along with their results. However, rerunning a Jupyter Notebook may not always generate identical results due to various factors, such as randomness, changes in library versions, or variations in the computational environment. This paper introduces the Similarity…

SensIAT: An R Package for Conducting Sensitivity Analysis of Randomized Trials with Irregular Assessment Times
Andrew Redd, Yujing Gao, Bonnie B. Smith, Ravi Varadhan, Andrea J. Apter, Daniel O. Scharfstein
https://arxiv.org/abs/2509.22389

SensIAT: An R Package for Conducting Sensitivity Analysis of Randomized Trials with Irregular Assessment Times
This paper introduces an R package SensIAT that implements a sensitivity analysis methodology, based on augmented inverse intensity weighting, for randomized trials with irregular and potentially informative assessment times. Targets of inference involve the population mean outcome in each treatment arm as well as the difference in these means (i.e., treatment effect) at specified times after randomization. This methodology is useful in settings where there is concern that study participants ar…

IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Santhosh G S, Akshay Govind S, Gokul S Krishnan, Balaraman Ravindran, Sriraam Natarajan
https://arxiv.org/abs/2510.02742

IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Large Language Models (LLMs) have gained significant traction across critical domains owing to their impressive contextual understanding and generative capabilities. However, their increasing deployment in high stakes applications necessitates rigorous evaluation of embedded biases, particularly in culturally diverse contexts like India where existing embedding-based bias assessment methods often fall short in capturing nuanced stereotypes. We propose an evaluation framework based on a encoder …

Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
Seungwon Choi, Donggyu Park, Seo-Yeon Hwang, Tae-Wan Kim
https://arxiv.org/abs/2510.01648 https://…

Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
A fundamental challenge in robust visual-inertial odometry (VIO) is to dynamically assess the reliability of sensor measurements. This assessment is crucial for properly weighting the contribution of each measurement to the state estimate. Conventional methods often simplify this by assuming a static, uniform uncertainty for all measurements. This heuristic, however, may be limited in its ability to capture the dynamic error characteristics inherent in real-world data. To improve this limitatio…

in der #verschlagwortung diese woche: "The Fountas & Pinnell literacy continuum: a tool of assessment, planning, and teaching" (https://katalogplus.uni-muenster.de/pe

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
Boris Kriuk
https://arxiv.org/abs/2510.02189 https://

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
Arctic warming threatens over 100 billion in permafrost-dependent infrastructure across Northern territories, yet existing risk assessment frameworks lack spatiotemporal validation, uncertainty quantification, and operational decision-support capabilities. We present a hybrid physics-machine learning framework integrating 2.9 million observations from 171,605 locations (2005-2021) combining permafrost fraction data with climate reanalysis. Our stacked ensemble model (Random Forest + Histogram G…

Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Mario Medrano-Paredes, Carmen Fern\'andez-Gonz\'alez, Francisco-Javier D\'iaz-Pernas, Hichem Saoudi, Javier Gonz\'alez-Alonso, Mario Mart\'inez-Zarzuela

Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Advances in machine learning and wearable sensors offer new opportunities for capturing and analyzing human movement outside specialized laboratories. Accurate assessment of human movement under real-world conditions is essential for telemedicine, sports science, and rehabilitation. This preclinical benchmark compares monocular video-based 3D human pose estimation models with inertial measurement units (IMUs), leveraging the VIDIMU dataset containing a total of 13 clinically relevant daily acti…

Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
Jiyeon Kim, Yingjie Hu, Negar Elhami-Khorasani, Kai Sun, Ryan Zhenqi Zhou
https://arxiv.org/abs/2509.21327

Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
Predicting the spread of wildfires is essential for effective fire management and risk assessment. With the fast advancements of artificial intelligence (AI), various deep learning models have been developed and utilized for wildfire spread prediction. However, there is limited understanding of the advantages and limitations of these models, and it is also unclear how deep learning-based fire spread models can be compared with existing non-AI fire models. In this work, we assess the ability of …

For objective news about Ukraine, read ISW.
https://understandingwar.org/research/russia-ukraine/russian-offensive-campaign-assessment-november-22-2025/

Russian Offensive Campaign Assessment, November 22, 2025
Russian officials continue to set information conditions to reject the US-proposed peace plan, indicating the Kremlin's intentions.

Series C, Episode 07 - Children of Auron
AVON: Maybe not, but dispassionately, without prejudice or emotion.
CALLY: So what's Orac's dispassionate assessment? A trap?
AVON: We haven't got that far.
ZEN: Distress signal from Auron.
CALLY: Details.
https://blake.torpidity.net/m/307/248

A Hybrid DNN Transformer AE Framework for Corporate Tax Risk Supervision and Risk Level Assessment
Zhenzhen Song, Nanxi Wang, Hongji Li
https://arxiv.org/abs/2509.23862 https://…

A Hybrid DNN Transformer AE Framework for Corporate Tax Risk Supervision and Risk Level Assessment
Tax risk supervision has become a critical component of modern financial governance, as irregular tax behaviors and hidden compliance risks pose significant challenges to regulatory authorities and enterprises alike. Traditional rule-based methods often struggle to capture complex and dynamic tax-related anomalies in large-scale enterprise data. To address this issue, this paper proposes a hybrid deep learning framework (DNN-Transformer-Autoencoder) for corporate tax risk supervision and risk l…

EEG-Based Framework for Reflexive and Perceptual Assessment in CLIS: Preliminary Study in Healthy Volunteers
Nicoli Leal, Rute Bettencourt, Urbano J. Nunes, Gabriel Pires
https://arxiv.org/abs/2509.23524

EEG-Based Framework for Reflexive and Perceptual Assessment in CLIS: Preliminary Study in Healthy Volunteers
Despite the general assumption that completely locked-in state (CLIS) patients remain conscious and aware of their environment, the effectiveness of brain-computer interfaces (BCIs) in facilitating communication has been limited, as reported both in the literature and in our own findings. This limitation is likely attributable to impairments in executive functions, working memory, and vigilance, which appear to hinder the establishment of reliable BCI-based communication. The main goal of this …

Predicting MacroB12: Data-Driven Assessment of Pre-PEG B12 Utility and Clinical Caveats
Carmen Fr\'ias-Ruiz (Hospital Universitario Nuestra Se\~nora de Candelaria), Jos\'e Mar\'ia G\'alvez-Navas (CIBERESP, Andalusian School of Public Health, Instituto de Investigaci\'on Biosanitaria ibs.GRANADA, University of Granada), Isabel Garc\'ia-Calcerrada (Hospital Universitario Nuestra Se\~nora de Candelaria), Silvia Heras Fl\'orez (Hospital Universitario Nuestra Se\…

Predicting MacroB12: Data-Driven Assessment of Pre-PEG B12 Utility and Clinical Caveats
MacroB12 interference presents a significant challenge in the diagnosis of Vitamin B12 status, potentially masking true deficiency. To establish robust predictors and quantify the utility of pre-polyethylene glycol (PEG) B12 levels, we conducted a retrospective analysis of 875 individuals with hypercobalaminaemia (greater than 1000 pg/mL), using multiple imputation to handle missing data. Multivariable regression modelling revealed that MacroB12 positivity (less than 30% PEG recovery) was indep…

Javonte Williams offers brutal assessment of Cowboys' run game vs. Panthers https://www.sportingnews.com/us/nfl/dallas-cowboys/news/javonte-williams-offers-brutal-assessment-cowboys-run-game…

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review
Andrii Zahorodnii, Jasper J. F. van den Bosch, Ian Charest, Christopher Summerfield, Ila R. Fiete

PLUTO: A Public Value Assessment Tool
Laura Koesten, P\'eter Ferenc Gyarmati, Connor Hogan, Bernhard Jordan, Seliem El-Sayed, Barbara Prainsack, Torsten M\"oller
https://arxiv.org/abs/2509.12773

PLUTO: A Public Value Assessment Tool
We present PLUTO (Public VaLUe Assessment TOol), a framework for assessing the public value of specific instances of data use. Grounded in the concept of data solidarity, PLUTO aims to empower diverse stakeholders - including regulatory bodies, private enterprises, NGOs, and individuals - to critically engage with data projects through a structured assessment of the risks and benefits of data use, and by encouraging critical reflection. This paper discusses the theoretical foundation, developme…

Vulnerability Analysis Evaluating Bilevel Optimal Power Flow Approaches for Multiple Load Cases
Eric T\"onges, Martin Braun, Philipp H\"artel
https://arxiv.org/abs/2510.01073

Vulnerability Analysis Evaluating Bilevel Optimal Power Flow Approaches for Multiple Load Cases
This work presents two methodologies to enhance vulnerability assessment in power systems using bilevel attacker-defender network interdiction models. First, we introduce a systematic evaluation procedure for comparing different optimal power flow formulations in the lower-level problem. We demonstrate the procedure for a comparison of the widely used DC approximation and a linearized AC optimal power flow model. Second, we propose a novel scoring methodology to identify and prioritize critical…

An intelligence assessment style-ish write up on the US by purported former professionals in the Intelligence Community (IC) who call themselves collectively The Steady State. Executive summary and scope statement seem like clear thinking.
https://steadystate1.substack.com/p/ac

Accelerating Authoritarian Dynamics: Assessment of Democratic Decline
The Steady State | October 16, 2025

Not everyone agrees...
https://youtube.com/shorts/gcO8dHeKjU0
But I think this assessment may overestimate the competence of the administration (they won't just crash things because their heads are just that full of shit), and may underestimate the ability of the administration (or really, the heritage foundation or other fash planners) to just make some shit up work around any limitation. The use of private donations on the ballroom and to fund military ops is a pretty clear test of that.
No matter what, the government will be shut down. All the things you care about will either be eliminated right now, or slowly over time. That's been happening since the 70's, and even faster since the 90's, so it shouldn't be surprising that it's happening now.
That's the scenario to prepare for, and you should prepare for it even if democrats somehow get control of the government again. Much of the public sector has been privatized and destroyed under democratic administrations.

Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
Vaishnavi Sharma, Rakesh Thakur, Shashwat Sharma, Kritika Panjanani
https://arxiv.org/abs/2509.25258

Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
Practical lab education in computer science often faces challenges such as plagiarism, lack of proper lab records, unstructured lab conduction, inadequate execution and assessment, limited practical learning, low student engagement, and absence of progress tracking for both students and faculties, resulting in graduates with insufficient hands-on skills. In this paper, we introduce AsseslyAI, which addresses these challenges through online lab allocation, a unique lab problem for each student, …

Comparative Study of Subjective Video Quality Assessment Test Methods in Crowdsourcing for Varied Use Cases
Babak Naderi, Ross Cutler
https://arxiv.org/abs/2509.20118 https://…

Comparative Study of Subjective Video Quality Assessment Test Methods in Crowdsourcing for Varied Use Cases
In crowdsourced subjective video quality assessment, practitioners often face a choice between Absolute Category Rating (ACR), ACR with Hidden Reference (ACR-HR), and Comparison Category Rating (CCR). We conducted a P.910-compliant, side-by-side comparison across six studies using 15 talking-head sources of good and fair quality, processed with realistic degradations (blur, scaling, compression, freezing, and their combinations), as well as a practical bitrate-ladder task at 720p and 1080p reso…

Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Ruyu Liu, Dongxu Zhuang, Jianhua Zhang, Arega Getaneh Abate, Per Sieverts Nielsen, Ben Wang, Xiufeng Liu
https://arxiv.org/abs/2510.00797

Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to addres…

An Initial Assessment of the Hanle Echelle Spectrograph for Exoplanet Atmosphere Studies
Manjunath Bestha, Athira Unni, T. Sivarani, Shivangi Menon, Parvathy M, Arun Surya, Pallavi Saraf, Devika Divakar, Lokesh Manickavasaham
https://arxiv.org/abs/2509.17142

An Initial Assessment of the Hanle Echelle Spectrograph for Exoplanet Atmosphere Studies
Transmission spectroscopy is an effective technique for probing exoplanetary atmospheres. While most observations have relied on space facilities such as HST and JWST, ground-based high-resolution transmission spectroscopy (HRTS) has also provided valuable insights by resolving individual atomic features. In this work, we present an initial performance assessment and feasibility test of the Hanle Echelle Spectrograph (HESP) on the 2 m Himalayan Chandra Telescope (HCT) for HRTS. As a benchmark, …

A Preliminary Assessment of Shipboard Power System Architectures for LVDC Integration
D. Roncagliolo (Univerisity of Genova), M. Gallo (Univerisity of Genova), D. Kaza (Cetena S.p.A), F. D'Agostino (Univerisity of Genova), A. Chiarelli (Fincantieri S.p.A), F. Silvestro (Univerisity of Genova)
https://arxiv.org/abs/2509.22567

A Preliminary Assessment of Shipboard Power System Architectures for LVDC Integration
The adoption of low-voltage direct current sections within grid architectures is emerging as a promising design option in the naval sector. This paper presents a preliminary comparative assessment of three different grid topologies, using an existing MVAC-LVAC shipboard power system as a reference: a conventional MVAC-LVAC radial distribution with an additional LVDC section, a full LVDC radial distribution and a zonal LVDC distribution. Each architecture includes typical elements such as synchr…

Patrick Mahomes gives blunt assessment of Chiefs' playoff hopes after loss to Cowboys: 'Have to win them all'

https://www.cbssports.com/nfl/news/patrick-mahome…

Chip Kelly Shares Honest Assessment on How Raiders OL Impacts Offense https://www.si.com/nfl/raiders/onsi/las-vegas-chip-kelly-shares-honest-assessment-impacts-offense

Regardless what the US and russian propaganda is saying, Russia is not winning...
"Data on Russian forces’ rate of advance indicates that a Russian military victory in Ukraine is not inevitable, and a rapid Russian seizure of the rest of Donetsk Oblast is not imminent. Russian advances elsewhere on the frontline have been opportunistic and exploited seasonal weather"

Russian Offensive Campaign Assessment, November 26, 2025
Data on Russian forces’ rate of advance indicates that a Russian military victory in Ukraine is not inevitable.

Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
Milan Gandhi, Peter Cihon, Owen Larter, Rebecca Anselmetti
https://arxiv.org/abs/2509.22742

Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
Risk assessments for advanced AI systems require evaluating both the models themselves and their deployment contexts. We introduce the Societal Capacity Assessment Framework (SCAF), an indicators-based approach to measuring a society's vulnerability, coping capacity, and adaptive capacity in response to AI-related risks. SCAF adapts established resilience analysis methodologies to AI, enabling organisations to ground risk management in insights about country-level deployment conditions. It can …

Democratic Representative Sara Jacobs said she was told in a Pentagon briefing
“that they do not need to positively identify individuals on the vessel to do the strikes”
and that was part of the reason why the administration has not sought to detain or prosecute the survivors of the strikes,
“because they could not satisfy the evidentiary burden.”
“There’s nothing that we heard in there that changes my assessment that this is completely illegal,
that it is unlaw…

Pentagon Admits It Has No Idea Who’s on “Drug Boats” Being Bombed
A Democratic lawmaker revealed the shocking detail after a Pentagon briefing for members of Congress.

Jerry Jones' assessment of George Pickens with Cowboys will surprise Steelers fans https://www.sportingnews.com/us/nfl/pittsburgh-steelers/news/jerry-jones-assessment-george-pickens-cowbo…

Reproducing a Security Risk Assessment Using Computer Aided Design
Avi Shaked
https://arxiv.org/abs/2509.16593 https://arxiv.org/pdf/2509.16593

Reproducing a Security Risk Assessment Using Computer Aided Design
Security risk assessment is essential in establishing the trustworthiness and reliability of modern systems. While various security risk assessment approaches exist, prevalent applications are "pen and paper" implementations that -- even if performed digitally using computers -- remain prone to authoring mistakes and inconsistencies. Computer-aided design approaches can transform security risk assessments into more rigorous and sustainable efforts. This is of value to both industrial practition…

AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework
Sofia Vei, Paolo Giudici, Pavlos Sermpezis, Athena Vakali, Adelaide Emma Bernardelli
https://arxiv.org/abs/2509.10104

AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework
The absolute dominance of Artificial Intelligence (AI) introduces unprecedented societal harms and risks. Existing AI risk assessment models focus on internal compliance, often neglecting diverse stakeholder perspectives and real-world consequences. We propose a paradigm shift to a human-centric, harm-severity adaptive approach grounded in empirical incident data. We present AI Harmonics, which includes a novel AI harm assessment metric (AIH) that leverages ordinal severity data to capture rela…

"Climate change is wreaking havoc on World Cultural Heritage sites, study finds"
#Climate #ClimateChange

Climate change is wreaking havoc on World Cultural Heritage sites, study finds
The Great Wall of China, Petra of Jordan, Ellora Caves of India, the traditional Ottoman Houses in Turkey — none of these world-famous historical sites can easily be replaced if damaged. Yet, each of these sits out in the open, inviting hordes of tourists every year, under alarming climate jolts. A recent global assessment revealed […]

Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Zheng-an Wang, Yanbo J. Wang, Jiachi Zhang, Qi Xu, Yilun Zhao, Jintao Li, Yipeng Zhang, Bo Yang, Xinkai Gao, Xiaofeng Cao, Kai Xu, Pengpeng Hao, Xuan Yang, Heng Fan
https://arxiv.org/abs/2509.13818

Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critical issue in inclusive finance where data scarcity and imbalance limit the effectiveness of conventional models. To address this, we design and implement a novel hybrid quantum-classical workflow. The methodology first employs an ensemble of classical machine learning models (Logi…

The UK's Cabinet Office says its Fraud Risk Assessment Accelerator AI tool helped the government recover £480M from fraud in the 12 months from April 2024 (Jack Fenwick/BBC)
https://www.bbc.com/news/articles/cpd92gpld0go

AI tool used to recover £500m lost to fraud, government says
Ministers say software developed by the Cabinet Office has helped to ramp up fraud detection.

A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation
Kleanthis Avramidis, Myzelle Hughes, Idan A Blank, Dani Byrd, Assal Habibi, Takfarinas Medani, Richard M Leahy, Shrikanth Narayanan
https://arxiv.org/abs/2510.00422

A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation
Accurate identification of mental health biomarkers can enable earlier detection and objective assessment of compromised mental well-being. In this study, we analyze electrodermal activity recorded during an Emotional Stroop task to capture sympathetic arousal dynamics associated with depression and suicidal ideation. We model the timing of skin conductance responses as a point process whose conditional intensity is modulated by task-based covariates, including stimulus valence, reaction time, …

DPC-QA Net: A No-Reference Dual-Stream Perceptual and Cellular Quality Assessment Network for Histopathology Images
Qijun Yang, Boyang Wang, Hujun Yin
https://arxiv.org/abs/2509.15802

DPC-QA Net: A No-Reference Dual-Stream Perceptual and Cellular Quality Assessment Network for Histopathology Images
Reliable whole slide imaging (WSI) hinges on image quality,yet staining artefacts, defocus, and cellular degradations are common. We present DPC-QA Net, a no-reference dual-stream network that couples wavelet-based global difference perception with cellular quality assessment from nuclear and membrane embeddings via an Aggr-RWKV module. Cross-attention fusion and multi-term losses align perceptual and cellular cues. Across different datasets, our model detects staining, membrane, and nuclear is…

I'm sure they performed a full security risk assessment before doing this. JFC.
“I logged on to find several emails tagged ‘External’ because DOGE just brought in their own servers and plugged them into the network.
https://www.wired.com/story/oral-history-doge-feder…

The Story of DOGE, as Told by Federal Workers
WIRED spoke with more than 200 federal workers in dozens of agencies to learn what happened as the Department of Government Efficiency tore through their offices.

Ben Johnson shares pointed assessment of Bears' practice habits: 'Yet to reflect a championship-caliber team'

https://www.cbssports.com/nfl/new…

Data-Driven Resilience Assessment against Sparse Sensor Attacks
Takumi Shinohara, Karl Henrik Johansson, Henrik Sandberg
https://arxiv.org/abs/2509.25064 https://

Data-Driven Resilience Assessment against Sparse Sensor Attacks
We present a data-driven framework for assessing the attack resilience of linear time-invariant systems against malicious false data injection sensor attacks. Based on the concept of sparse observability, data-driven resilience metrics are proposed. First, we derive a data-driven necessary and sufficient condition for assessing the system's resilience against sensor attacks, using data collected without any attacks. If we obtain attack-free data that satisfy a specific rank condition, we can ex…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Partial Identification Approach to Counterfactual Fairness Assessment
Saeyoung Rho, Junzhe Zhang, Elias Bareinboim

Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based 3D Reconstruction
Mariadas Capsran Roshan, Edgar M Hidalgo, Mats Isaksson, Michelle Dunn, Jagannatha Charjee Pyaraka
https://arxiv.org/abs/2509.24867

Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based 3D Reconstruction
Echocardiography is a key imaging modality for cardiac assessment but remains highly operator-dependent, and access to trained sonographers is limited in underserved settings. Teleoperated robotic echocardiography has been proposed as a solution; however, clinical studies report longer examination times than manual procedures, increasing diagnostic delays and operator workload. Automating non-expert tasks, such as automatically moving the probe to an ideal starting pose, offers a pathway to red…

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Ke Wang, Wenning Wei, Yan Deng, Lei He, Sheng Zhao
https://arxiv.org/abs/2509.15701 https://

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Automatic Pronunciation Assessment (APA) is critical for Computer-Assisted Language Learning (CALL), requiring evaluation across multiple granularities and aspects. Large Multimodal Models (LMMs) present new opportunities for APA, but their effectiveness in fine-grained assessment remains uncertain. This work investigates fine-tuning LMMs for APA using the Speechocean762 dataset and a private corpus. Fine-tuning significantly outperforms zero-shot settings and achieves competitive results on si…

A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor
Marcin Jukiewicz
https://arxiv.org/abs/2509.26483

A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor
This study presents the first large-scale, side-by-side comparison of contemporary Large Language Models (LLMs) in the automated grading of programming assignments. Drawing on over 6,000 student submissions collected across four years of an introductory programming course, we systematically analysed the distribution of grades, differences in mean scores and variability reflecting stricter or more lenient grading, and the consistency and clustering of grading patterns across models. Eighteen pub…

AEAS: Actionable Exploit Assessment System
Xiangmin Shen, Wenyuan Cheng, Yan Chen, Zhenyuan Li, Yuqiao Gu, Lingzhi Wang, Wencheng Zhao, Dawei Sun, Jiashui Wang
https://arxiv.org/abs/2509.17832

AEAS: Actionable Exploit Assessment System
Security practitioners face growing challenges in exploit assessment, as public vulnerability repositories are increasingly populated with inconsistent and low-quality exploit artifacts. Existing scoring systems, such as CVSS and EPSS, offer limited support for this task. They either rely on theoretical metrics or produce opaque probability estimates without assessing whether usable exploit code exists. In practice, security teams often resort to manual triage of exploit repositories, which is …

Investigation of Using Non-Contact Electrodes for Fetal ECG Monitoring
Tai Le, Hau Luu, Loan Pham-Nguyen, Hung Viet-Dao, Duc Nguyen Minh, Afshan B. Hameed, Hoang Nguyen, Liem Thanh Nguyen, Huy-Dung Han, Hung Cao
https://arxiv.org/abs/2510.00550

Investigation of Using Non-Contact Electrodes for Fetal ECG Monitoring
Regular physiological monitoring of maternal and fetal parameters is indispensable for ensuring safe outcomes during pregnancy and parturition. Fetal electrocardiogram (fECG) assessment is crucial to detect fetal distress and developmental anomalies. Given challenges of prenatal care due to the lack of medical professionals and the limit of accessibility, especially in remote and resource-poor areas, we develop a fECG monitoring system using novel non-contact electrodes (NCE) to record the feta…

"I think this is fair'': Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
Lin Luo, Yuri Nakao, Mathieu Chollet, Hiroya Inakoshi, Simone Stumpf
https://arxiv.org/abs/2509.17956

"I think this is fair'': Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 30 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness wh…

AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant
Fadjimata I Anaroua, Qing Li, Yan Tang, Hong P. Liu
https://arxiv.org/abs/2509.20369

AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant
This paper presents VITA (Virtual Teaching Assistants), an adaptive distributed learning (ADL) platform that embeds a large language model (LLM)-powered chatbot (BotCaptain) to provide dialogic support, interoperable analytics, and integrity-aware assessment for workforce preparation in data science. The platform couples context-aware conversational tutoring with formative-assessment patterns designed to promote reflective reasoning. The paper describes an end-to-end data pipeline that transfor…

kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
Jenna Kline, Maksim Kholiavchenko, Samuel Stevens, Nina van Tiel, Alison Zhong, Namrata Banerji, Alec Sheets, Sowbaranika Balasubramaniam, Isla Duporge, Matthew Thompson, Elizabeth Campolongo, Jackson Miliko, Neil Rosser, Tanya Berger-Wolf, Charles V. Stewart, Daniel I. Rubenstein
https://

kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
A comprehensive understanding of animal behavior ecology depends on scalable approaches to quantify and interpret complex, multidimensional behavioral patterns. Traditional field observations are often limited in scope, time-consuming, and labor-intensive, hindering the assessment of behavioral responses across landscapes. To address this, we present kabr-tools (Kenyan Animal Behavior Recognition Tools), an open-source package for automated multi-species behavioral monitoring. This framework in…

"The World Is Failing to Slow the Loss of Its Forests"
#Trees #Forests #Environment

The World Is Failing to Slow the Loss of Its Forests

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/6]:
- Assessment of deep learning models integrated with weather and environmental variables for wildfi...
Jiyeon Kim, Yingjie Hu, Negar Elhami-Khorasani, Kai Sun, Ryan Zhenqi Zhou

Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Hong-Yun Lin, Jhen-Ke Lin, Chung-Chun Wang, Hao-Chien Lu, Berlin Chen
https://arxiv.org/abs/2509.16025

Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Spoken Language Assessment (SLA) estimates a learner's oral proficiency from spontaneous speech. The growing population of L2 English speakers has intensified the demand for reliable SLA, a critical component of Computer Assisted Language Learning (CALL). Existing efforts often rely on cascaded pipelines, which are prone to error propagation, or end-to-end models that often operate on a short audio window, which might miss discourse-level evidence. This paper introduces a novel multimodal found…

FROQ: Observing Face Recognition Models for Efficient Quality Assessment
\v{Z}iga Babnik, Deepak Kumar Jain, Peter Peer, Vitomir \v{S}truc
https://arxiv.org/abs/2509.17689 https…

FROQ: Observing Face Recognition Models for Efficient Quality Assessment
Face Recognition (FR) plays a crucial role in many critical (high-stakes) applications, where errors in the recognition process can lead to serious consequences. Face Image Quality Assessment (FIQA) techniques enhance FR systems by providing quality estimates of face samples, enabling the systems to discard samples that are unsuitable for reliable recognition or lead to low-confidence recognition decisions. Most state-of-the-art FIQA techniques rely on extensive supervised training to achieve a…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/12]:
- Safety Assessment of Scaffolding on Construction Site using AI
Sameer Prabhu, Amit Patwardhan, Ramin Karim

Responsible AI Technical Report
KT, :, Soonmin Bae, Wanjin Park, Jeongyeop Kim, Yunjin Park, Jungwon Yoon, Junhyung Moon, Myunggyo Oh, Wonhyuk Lee, Junseo Jang, Dongyoung Jung, Minwook Ju, Eunmi Kim, Sujin Kim, Youngchol Kim, Somin Lee, Wonyoung Lee, Minsung Noh, Hyoungjun Park, Eunyoung Shin
https://arxiv.org/abs/2509.20057

Responsible AI Technical Report
KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT'…

Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence
Yuqiao Meng, Luoxi Tang, Feiyang Yu, Jinyuan Jia, Guanhua Yan, Ping Yang, Zhaohan Xi
https://arxiv.org/abs/2509.23573

Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence (CTI) to support vulnerability assessment and incident response. While recent work has shown that LLMs can support a wide range of CTI tasks such as threat analysis, vulnerability detection, and intrusion defense, significant performance gaps persist in practical deployments. In this paper, we investigate the intrins…

Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking
Jeonghyun Lee, Jui-Tse Hung, Meryem Yilmaz Soylu, Diana Popescu, Christopher Zhang Cui, Gayane Grigoryan, David A Joyner, Stephen W Harmon
https://arxiv.org/abs/2509.16262

Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking
This study examines the impact of Socratic Mind, a Generative Artificial Intelligence (GenAI) powered formative assessment tool that employs Socratic questioning to support student learning in a large, fully online undergraduate-level computing course. Employing a quasi-experimental, mixed-methods design, we investigated participants' engagement patterns, the influence of user experience on engagement, and impacts on both perceived and actual learning outcomes. Data were collected from the syst…

Predictive Quality Assessment for Mobile Secure Graphics
Cas Steigstra, Sergey Milyaev, Shaodi You
https://arxiv.org/abs/2509.20028 https://arxiv.org/pdf/2…

Predictive Quality Assessment for Mobile Secure Graphics
The reliability of secure graphic verification, a key anti-counterfeiting tool, is undermined by poor image acquisition on smartphones. Uncontrolled user captures of these high-entropy patterns cause high false rejection rates, creating a significant 'reliability gap'. To bridge this gap, we depart from traditional perceptual IQA and introduce a framework that predictively estimates a frame's utility for the downstream verification task. We propose a lightweight model to predict a quality score…

Automated Vulnerability Validation and Verification: A Large Language Model Approach
Alireza Lotfi, Charalampos Katsis, Elisa Bertino
https://arxiv.org/abs/2509.24037 https://…

Automated Vulnerability Validation and Verification: A Large Language Model Approach
Software vulnerabilities remain a critical security challenge, providing entry points for attackers into enterprise networks. Despite advances in security practices, the lack of high-quality datasets capturing diverse exploit behavior limits effective vulnerability assessment and mitigation. This paper introduces an end-to-end multi-step pipeline leveraging generative AI, specifically large language models (LLMs), to address the challenges of orchestrating and reproducing attacks to known softw…

A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
MohammadAli Hamidi, Hadi Amirpour, Luigi Atzori, Christian Timmerer
https://arxiv.org/abs/2509.10114

A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems, especially in uncontrolled, real-world environments. Although several methods have been proposed, general-purpose no-reference image quality assessment techniques often fail to capture face-specific degradations. Meanwhile, state-of-the-art FIQA models tend to be computationally intensive, limiting their practical applicability. We propose a lightweight and efficient method for FIQA, designe…

Beyond the Score: Uncertainty-Calibrated LLMs for Automated Essay Assessment
Ahmed Karim (Judy), Qiao Wang (Judy), Zheng Yuan
https://arxiv.org/abs/2509.15926 https://

Beyond the Score: Uncertainty-Calibrated LLMs for Automated Essay Assessment
Automated Essay Scoring (AES) systems now reach near human agreement on some public benchmarks, yet real-world adoption, especially in high-stakes examinations, remains limited. A principal obstacle is that most models output a single score without any accompanying measure of confidence or explanation. We address this gap with conformal prediction, a distribution-free wrapper that equips any classifier with set-valued outputs and formal coverage guarantees. Two open-source large language models…

Bias in the Picture: Benchmarking VLMs with Social-Cue News Images and LLM-as-Judge Assessment
Aravind Narayanan, Vahid Reza Khazaie, Shaina Raza
https://arxiv.org/abs/2509.19659

Bias in the Picture: Benchmarking VLMs with Social-Cue News Images and LLM-as-Judge Assessment
Large vision-language models (VLMs) can jointly interpret images and text, but they are also prone to absorbing and reproducing harmful social stereotypes when visual cues such as age, gender, race, clothing, or occupation are present. To investigate these risks, we introduce a news-image benchmark consisting of 1,343 image-question pairs drawn from diverse outlets, which we annotated with ground-truth answers and demographic attributes (age, gender, race, occupation, and sports). We evaluate a…

AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment
Kun Li, Lai-Man Po, Hongzheng Yang, Xuyuan Xu, Kangcheng Liu, Yuzhi Zhao
https://arxiv.org/abs/2509.11620

AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment
Multimodal Large Language Models (MLLMs) are increasingly applied in Personalized Image Aesthetic Assessment (PIAA) as a scalable alternative to expert evaluations. However, their predictions may reflect subtle biases influenced by demographic factors such as gender, age, and education. In this work, we propose AesBiasBench, a benchmark designed to evaluate MLLMs along two complementary dimensions: (1) stereotype bias, quantified by measuring variations in aesthetic evaluations across demograph…

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
Mohamed Basem, Mohamed Younes, Seif Ahmed, Abdelrahman Moustafa
https://arxiv.org/abs/2509.10040

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
We present MSAs winning system for the BAREC 2025 Shared Task on fine-grained Arabic readability assessment, achieving first place in six of six tracks. Our approach is a confidence-weighted ensemble of four complementary transformer models (AraBERTv2, AraELECTRA, MARBERT, and CAMeLBERT) each fine-tuned with distinct loss functions to capture diverse readability signals. To tackle severe class imbalance and data scarcity, we applied weighted training, advanced preprocessing, SAMER corpus relabe…

Image Realness Assessment and Localization with Multimodal Features
Lovish Kaushik, Agnij Biswas, Somdyuti Paul
https://arxiv.org/abs/2509.13289 https://ar…

Image Realness Assessment and Localization with Multimodal Features
A reliable method of quantifying the perceptual realness of AI-generated images and identifying visually inconsistent regions is crucial for practical use of AI-generated images and for improving photorealism of generative AI via realness feedback during training. This paper introduces a framework that accomplishes both overall objective realness assessment and local inconsistency identification of AI-generated images using textual descriptions of visual inconsistencies generated by vision-lang…

Selecting Optimal Camera Views for Gait Analysis: A Multi-Metric Assessment of 2D Projections
Dong Chen, Huili Peng, Yong Hu, Kenneth MC. Cheung
https://arxiv.org/abs/2509.17805

Selecting Optimal Camera Views for Gait Analysis: A Multi-Metric Assessment of 2D Projections
Objective: To systematically quantify the effect of the camera view (frontal vs. lateral) on the accuracy of 2D markerless gait analysis relative to 3D motion capture ground truth. Methods: Gait data from 18 subjects were recorded simultaneously using frontal, lateral and 3D motion capture systems. Pose estimation used YOLOv8. Four metrics were assessed to evaluate agreement: Dynamic Time Warping (DTW) for temporal alignment, Maximum Cross-Correlation (MCC) for signal similarity, Kullback-Leibl…

Shedding Light on Depth: Explainability Assessment in Monocular Depth Estimation
Lorenzo Cirillo, Claudio Schiavella, Lorenzo Papa, Paolo Russo, Irene Amerini
https://arxiv.org/abs/2509.15980

Shedding Light on Depth: Explainability Assessment in Monocular Depth Estimation
Explainable artificial intelligence is increasingly employed to understand the decision-making process of deep learning models and create trustworthiness in their adoption. However, the explainability of Monocular Depth Estimation (MDE) remains largely unexplored despite its wide deployment in real-world applications. In this work, we study how to analyze MDE networks to map the input image to the predicted depth map. More in detail, we investigate well-established feature attribution methods, …

TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models
Junyi Zhang, Jia-Chen Gu, Wenbo Hu, Yu Zhou, Robinson Piramuthu, Nanyun Peng
https://arxiv.org/abs/2509.25143

TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models
Existing medical reasoning benchmarks for vision-language models primarily focus on analyzing a patient's condition based on an image from a single visit. However, this setting deviates significantly from real-world clinical practice, where doctors typically refer to a patient's historical conditions to provide a comprehensive assessment by tracking their changes over time. In this paper, we introduce TemMed-Bench, the first benchmark designed for analyzing changes in patients' conditions betwe…