Tootfinder

A quantitative assessment of EOG eye tracking during free viewing in sighted and in congenitally blind https://doi.org/10.1016/j.brainres.2025.149794
"Our work further emphasizes the presence of blinks and saccadic muscular movements both in sighted individuals with…

Trump throws Gabbard under the bus -- again (Eli Stokols/Politico)
https://www.politico.com/news/2025/06/20/trump-gabbard-dismiss-iran-assessment-00415805
http://www.memeorandum.com/250621/p5#a250621p5

It would be very interesting to see the RAMS and risk assessment for installing the Butts exhibition at the #WhitechapelGallery.
#Registrar
#RiskAssessment

PRISON: Unmasking the Criminal Potential of Large Language Models
Xinyi Wu, Geng Hong, Pei Chen, Yueyue Chen, Xudong Pan, Min Yang
https://arxiv.org/abs/2506.16150

PRISON: Unmasking the Criminal Potential of Large Language Models
As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessment of their criminal capability in realistic interactions. We propose a unified framework PRISON, to quantify LLMs' criminal potential across five dimensions: False Statements, Frame-Up, Psychological Manipulation, Emotional Disguise, and Moral Disengagement. Using structured crime scenarios adapted from classic film…

Bills WR Keon Coleman gives harsh self-assessment of rookie season: 'When that (expletive) trash, you got to be better' https://www.nfl.com/news/bills-wr-keon-coleman-gives-harsh-self-assessment-of-rookie-season-yo…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
Mohsen Jenadeleh, Jon Sneyers, Davi Lazzarotto, Shima Mohammadi, Dominik Keller, Atanas Boev, Rakesh Rao Ramachandra Rao, Ant\'onio Pinheiro, Thomas Richter, Alexander Raake, Touradj Ebrahimi, Jo\~ao Ascenso, Dietmar Saupe
https://arxiv…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual…

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Yusdivia Molina-Rom\'an, David G\'omez-Ortiz, Ernestina Menasalvas-Ruiz, Jos\'e Gerardo Tamez-Pe\~na, Alejandro Santos-D\'iaz
https://arxiv.org/abs/2506.13964

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification a…

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Miguel A. Lago, Ghada Zamzmi, Brandon Eich, Jana G. Delfino
https://arxiv.org/abs/2506.13917

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Explainability features are intended to provide insight into the internal mechanisms of an AI device, but there is a lack of evaluation techniques for assessing the quality of provided explanations. We propose a framework to assess and report explainable AI features. Our evaluation framework for AI explainability is based on four criteria: 1) Consistency quantifies the variability of explanations to similar inputs, 2) Plausibility estimates how close the explanation is to the ground truth, 3) F…

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
Long-Vu Hoang, Tuan Nguyen, Tran Huy Dat
https://arxiv.org/abs/2506.14148

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and capturing the scattered signals from head-with-hair-sample objects, we classify hair type and moisture using AI-driven, deep-learning-based sound classification. We benchmark compr…

Towards Effective Complementary Security Analysis using Large Language Models
Jonas Wagner, Simon M\"uller, Christian N\"ather, Jan-Philipp Stegh\"ofer, Andreas Both
https://arxiv.org/abs/2506.16899

Towards Effective Complementary Security Analysis using Large Language Models
A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing (SAST) tools. Numerous false positives (FPs) in these reports reduce the effectiveness of security analysis. We propose using Large Language Models (LLMs) to improve the assessment of SAST findings. We investigate the ability of LLMs to reduce FPs while trying to maintain a perfect true positive rate, using datasets extracted from the OWASP Benchmark (v…

Quality Assessment of Python Tests Generated by Large Language Models
Victor Alves, Carla Bezerra, Ivan Machado, Larissa Rocha, T\'assio Virg\'inio, Publio Silva
https://arxiv.org/abs/2506.14297

Quality Assessment of Python Tests Generated by Large Language Models
The manual generation of test scripts is a time-intensive, costly, and error-prone process, indicating the value of automated solutions. Large Language Models (LLMs) have shown great promise in this domain, leveraging their extensive knowledge to produce test code more efficiently. This study investigates the quality of Python test code generated by three LLMs: GPT-4o, Amazon Q, and LLama 3.3. We evaluate the structural reliability of test suites generated under two distinct prompt contexts: Te…

StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery
Jina Kim, Leeje Jang, Yao-Yi Chiang, Guanyu Wang, Michelle Pasco
https://arxiv.org/abs/2506.14670

StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery
Traditionally, neighborhood studies have employed interviews, surveys, and manual image annotation guided by detailed protocols to identify environmental characteristics, including physical disorder, decay, street safety, and sociocultural symbols, and to examine their impact on developmental and health outcomes. While these methods yield rich insights, they are time-consuming and require intensive expert intervention. Recent technological advances, including vision-language models (VLMs), have…

Quantum Radar and Research Assessment
Gaspare Galati, Gabriele Pavan, Frederick Daum
https://arxiv.org/abs/2506.13797 https://arxiv.o…

Quantum Radar and Research Assessment
Quantum Radar was studied in many Nations for about fifteen years with the production of some hundred publications. In the post 2020 literature, it is shown that, due to the exceedingly low transmitted power, Quantum Radar cannot produce neither significant results nor real world applications. Regrettably, most of the scientific community ignored this negative outcome: a fact worth of exam. A detailed study of such an assessment failure depicts the main shortcomings of the present situation, ca…

On the off chance that the U.S. Government removes it from the web, this is the March 25, 2025 testimony of the U.S. Director of National Intelligence Tulsi Gabbard that Iran is _not_ currently developing nuclear weapons and it is at least 2 to 3 years from having a bomb ("breakout").
source:

lcpy: an open-source python package for parametric and dynamic Life Cycle Assessment and Life Cycle Costing
Spiros Gkousis, Evina Katsou
https://arxiv.org/abs/2506.13744

lcpy: an open-source python package for parametric and dynamic Life Cycle Assessment and Life Cycle Costing
This article describes lcpy, an open-source python package that allows for advanced parametric Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) analysis. The package is designed to allow the user to model a process with a flexible, modular design based on dictionaries and lists. The modeling can consider in-time variations, uncertainty, and allows for dynamic analysis, uncertainty assessment, as well as conventional static LCA and LCC. The package is compatible with optimization and unc…

Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Weicong Qin, Yi Xu, Weijie Yu, Teng Shi, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, Jun Xu
https://arxiv.org/abs/2506.14437

Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of 'value' labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for perso…

Life cycle assessment tools for road design: analysing linearity assumptions
Nikolaos Kalyviotis
https://arxiv.org/abs/2506.13896 https://

Life cycle assessment tools for road design: analysing linearity assumptions
Road infrastructure significantly impacts how people move and live and the emissions associated with travel behaviour. The design of roads is crucial in mitigating emissions. This paper reviews existing transport life cycle assessment tools that have been developed by various entities and can be used for roads. The review focuses on data sources used in the analysis, methods of estimating carbon dioxide emissions, the underlying software that is used to make the estimates, and any limitations o…

Error-Guided Pose Augmentation: Enhancing Rehabilitation Exercise Assessment through Targeted Data Generation
Omar Sherif, Ali Hamdi
https://arxiv.org/abs/2506.09833

Error-Guided Pose Augmentation: Enhancing Rehabilitation Exercise Assessment through Targeted Data Generation
Effective rehabilitation assessment is essential for monitoring patient progress, particularly in home-based settings. Existing systems often face challenges such as data imbalance and difficulty detecting subtle movement errors. This paper introduces Error-Guided Pose Augmentation (EGPA), a method that generates synthetic skeleton data by simulating clinically relevant movement mistakes. Unlike standard augmentation techniques, EGPA targets biomechanical errors observed in rehabilitation. Comb…

A Novel Approach to Differential Privacy with Alpha Divergence
Yifeng Liu, Zehua Wang
https://arxiv.org/abs/2506.17012 https://arxiv.…

A Novel Approach to Differential Privacy with Alpha Divergence
As data-driven technologies advance swiftly, maintaining strong privacy measures becomes progressively difficult. Conventional $(ε, δ)$-differential privacy, while prevalent, exhibits limited adaptability for many applications. To mitigate these constraints, we present alpha differential privacy (ADP), an innovative privacy framework grounded in alpha divergence, which provides a more flexible assessment of privacy consumption. This study delineates the theoretical underpinnings of ADP and co…

Bad moon rising…
("releasing" hundreds)
https://www.nytimes.com/2025/04/28/climate/national-climate-assessment-authors-dismissed.html?unlocked_article_cod…

'In the local data that the audit examined from three [THREE] police forces, they identified clear evidence of “over-representation among suspects of Asian and Pakistani-heritage men”.
'However, the review also criticised the ongoing failure to collect ethnicity at a national level, with it not recorded for two-thirds of perpetrators, making it impossible “to provide any accurate assessment from the nationally collected data”.'
Not so conclusive then.

What is the Casey report on UK grooming gangs – and why did Labour U-turn?
The report addressed the UK’s institutional failure to protect children and teenage girls from sexual exploitation.

A federal judge on Monday declared some of the Trump administration’s cuts to National Institutes of Health grants
“void and illegal,”
accusing the government of racial discrimination and prejudice against L.G.B.T.Q. individuals.
Ruling from the bench, Judge William G. Young of the Federal District Court for the District of Massachusetts
delivered a damning assessment of the Trump administrations’ motives in targeting hundreds of grants that focused on the health of Bl…

Trump Live Updates: Senate Republicans Propose Deeper Medicaid Cuts in Major Policy Bill

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdos...
Mullen, Harris, Rock, Thompson, Slavova, Talbert, Bumgardner

FPF and OneTrust launch updated Conformity Assessment under the EU AI Act: guide and infographic
https://fpf.org/blog/fpf-and-onetrust-launch-updated-conformity-assessment-under-the-eu-ai-act-guide-and-infographi…

VulStamp: Vulnerability Assessment using Large Language Model
Haoshen, Ming Hu, Xiaofei Xie, Jiaye Li, Mingsong Chen
https://arxiv.org/abs/2506.11484 https…

VulStamp: Vulnerability Assessment using Large Language Model
Although modern vulnerability detection tools enable developers to efficiently identify numerous security flaws, indiscriminate remediation efforts often lead to superfluous development expenses. This is particularly true given that a substantial portion of detected vulnerabilities either possess low exploitability or would incur negligible impact in practical operational environments. Consequently, vulnerability severity assessment has emerged as a critical component in optimizing software dev…

Die EU-Kommission treibt die neue EU-weite #Vorratsdatenspeicherung voran: Teilt der EU-Kommission eure Meinung zu dem Thema bis zum 18. Juni 2025 mit. #VDS

Improving Speech Enhancement with Multi-Metric Supervision from Learned Quality Assessment
Wei Wang, Wangyou Zhang, Chenda Li, Jiatong Shi, Shinji Watanabe, Yanmin Qian
https://arxiv.org/abs/2506.12260

Improving Speech Enhancement with Multi-Metric Supervision from Learned Quality Assessment
Speech quality assessment (SQA) aims to predict the perceived quality of speech signals under a wide range of distortions. It is inherently connected to speech enhancement (SE), which seeks to improve speech quality by removing unwanted signal components. While SQA models are widely used to evaluate SE performance, their potential to guide SE training remains underexplored. In this work, we investigate a training framework that leverages a SQA model, trained to predict multiple evaluation metri…

Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies
Matthias Monnereau, Ana Jarne, Axel Benoist, Cl\'emence Fradet, Maurice Perol, Thomas Filleron, Louise Baschet
https://arxiv.org/abs/2506.11587

Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies
To evaluate methodological challenges and regulatory considerations of indirect treatment comparisons (ITCs) with the analysis of international health technology assessment guidelines and French Transparency Committee (TC) decisions. We conducted a pragmatic review of ITC guidelines from major health technology assessment (HTA) bodies and multistakeholder organizations. Then, we analyzed TC opinions published between 2021-2023. We extracted data on ITC methodology, therapeutic areas, acceptabil…

Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
Mohammad Saleh Torkestani, Taha Mansouri
https://arxiv.org/abs/2506.02046 https:…

Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
This paper presents a theoretical framework for addressing the challenges posed by generative artificial intelligence (AI) in higher education assessment through a machine-versus-machine approach. Large language models like GPT-4, Claude, and Llama increasingly demonstrate the ability to produce sophisticated academic content, traditional assessment methods face an existential threat, with surveys indicating 74-92% of students experimenting with these tools for academic purposes. Current respon…

Global ocean acidification has passed safe planetary boundary threshold: Study https://news.mongabay.com/short-article/2025/06/global-ocean-acidification-has-passed-safe-planetary-boundary-threshold-study/

Global ocean acidification has passed safe planetary boundary threshold: Study
A new assessment finds that the world’s oceans crossed the safe threshold for acidification in 2020, breaching a key planetary boundary and posing serious threats to marine life. Ocean acidification is caused when excess atmospheric carbon dioxide, resulting from human activities like burning fossil fuels, dissolves in seawater, forming carbonic acid that increases the water’s […]

Learning Quality from Complexity and Structure: A Feature-Fused XGBoost Model for Video Quality Assessment
Amritha Premkumar, Prajit T Rajendran, Vignesh V Menon
https://arxiv.org/abs/2506.09795

Learning Quality from Complexity and Structure: A Feature-Fused XGBoost Model for Video Quality Assessment
This paper presents a novel approach for reduced-reference video quality assessment (VQA), developed as part of the recent VQA Grand Challenge. Our method leverages low-level complexity and structural information from reference and test videos to predict perceptual quality scores. Specifically, we extract spatio-temporal features using Video Complexity Analyzer (VCA) and compute SSIM values from the test video to capture both texture and structural characteristics. These features are aggregated…

We all keep our balance, our awareness of position in space, by analysing feedback we get from receivers in our skin and joints (proprioceptors) and from our visual assessment of our position in space. I am a below knee #amputee, so I have lost the position sensors in my foot, ankle, and leg. I gauge the location of my leg in space through my knee and secondarily, through my hip. 🧵

US spies said Iran wasn't building a nuclear weapon. Trump dismisses that assessment (Associated Press)
https://apnews.com/article/gabbard-trump-intelligence-iran-nuclear-program-51c8d85d536f8628870c110ac05bb518
http://www.memeorandum.com/250617/p152#a250617p152

Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation
Alex C. Newkirk, Jared Fernandez, Jonathan Koomey, Imran Latif, Emma Strubell, Arman Shehabi, Constantine Samaras
https://arxiv.org/abs/2506.14551

Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation
As AI's energy demand continues to grow, it is critical to enhance the understanding of characteristics of this demand, to improve grid infrastructure planning and environmental assessment. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-GPU H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design pow…

Vulnerability-Based Optimal Grid Defense Strategies for Enhancing Cyber-Physical Energy System Resilience
Eric T\"onges, Philipp H\"artel, Martin Braun
https://arxiv.org/abs/2506.09766

Vulnerability-Based Optimal Grid Defense Strategies for Enhancing Cyber-Physical Energy System Resilience
An approach is proposed to identify optimal asset protection strategies based on vulnerability assessment outcomes. Traditional bilevel attacker-defender models emphasize worstcase scenarios but offer limited defensive guidance. In contrast, trilevel models introduce high computational complexity and rely on fixed network configurations. The proposed critical-components method leverages vulnerability assessment results to determine protection strategies, effectively outsourcing the upper-level …

Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Yonggang Tao, Jingling Xue
https://arxiv.org/abs/2506.11614 https://

Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Delta debugging assumes search space monotonicity: if a program causes a failure, any supersets of that program will also induce the same failure, permitting the exclusion of subsets of non-failure-inducing programs. However, this assumption does not always hold in practice. This paper introduces Probabilistic Monotonicity Assessment (PMA), enhancing the efficiency of DDMIN-style algorithms without sacrificing effectiveness. PMA dynamically models and assesses the search space's monotonicity ba…

'Israel is estimated to possess 90 nuclear warheads and may have the ability to launch attacks with them by land, sea, and air.'
'“We continue to assess Iran is not building a nuclear weapon and that [Iran’s supreme leader, Ayatollah Ali] Khamenei has not reauthorized the nuclear weapons program he suspended in 2003, though pressure has probably built on him to do so,” reads the [USA] 2025 Annual Threat Assessment.'

U.S. Intel Says Iran Isn’t a Nuclear Threat. Israel Wants the U.S. to Bomb It Anyway.
The U.S. is helping Israel wage war on Iran over its nuclear program. But U.S. intelligence says Iran is not building a bomb.

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
Patrick Sutanto, Jonathan Kenrick, Max Lorenz, Joan Santoso
https://arxiv.org/abs/2506.13785

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
The application of Large Language Models (LLMs) to text-to-SQL tasks promises to democratize data access, particularly in critical industries like aviation Maintenance, Repair, and Operation (MRO). However, progress is hindered by two key challenges: the rigidity of conventional evaluation metrics such as execution accuracy, which offer coarse, binary feedback, and the scarcity of domain-specific evaluation datasets. This paper addresses these gaps. To enable more nuanced assessment, we introdu…

"Russia's only real hope of winning this war is to convince the West to abandon Ukraine. " a key sentence from https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-may-25-2025 That is more an…

A Study on Speech Assessment with Visual Cues
Shafique Ahmed, Ryandhimas E. Zezario, Nasir Saleem, Amir Hussain, Hsin-Min Wang, Yu Tsao
https://arxiv.org/abs/2506.09549

A Study on Speech Assessment with Visual Cues
Non-intrusive assessment of speech quality and intelligibility is essential when clean reference signals are unavailable. In this work, we propose a multimodal framework that integrates audio features and visual cues to predict PESQ and STOI scores. It employs a dual-branch architecture, where spectral features are extracted using STFT, and visual embeddings are obtained via a visual encoder. These features are then fused and processed by a CNN-BLSTM with attention, followed by multi-task learn…

(AI peers) are people learning from the same standpoint: Perception of AI characters in a Collaborative Science Investigation
Eunhye Grace Ko, Soo Hyoung Joo
https://arxiv.org/abs/2506.06165

(AI peers) are people learning from the same standpoint: Perception of AI characters in a Collaborative Science Investigation
While the complexity of 21st-century demands has promoted pedagogical approaches to foster complex competencies, a persistent gap remains between in-class learning activities and individualized learning or assessment practices. To address this, studies have explored the use of AI-generated characters in learning and assessment. One attempt is scenario-based assessment (SBA), a technique that not only measures but also fosters the development of competencies throughout the assessment process. SB…

Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
Stefan Orf, Sven Ochs, Valentin Marotta, Oliver Conder, Marc Ren\'e Zofka, J. Marius Z\"ollner
https://arxiv.org/abs/2506.02922

Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of individual components an isolated glimpse into the system is given. Literature provides several of these detached assessments, often in the form of safety or performance measures. But dependencies, redundancies, error propagation and conflicting functionality statements do not allow for easy combination of these measures…

Impact of Pre-Assessment and Post-Assessment in an Introductory Real Analysis Course
Chamila Malagoda Gamage
https://arxiv.org/abs/2505.22479 https://

Impact of Pre-Assessment and Post-Assessment in an Introductory Real Analysis Course
This study explores how pre- and post-assessments shape learning outcomes in an Introductory Real Analysis course. Pre-assessments act as learning roadmaps, highlighting prior knowledge and guiding student focus, while post-assessments measure growth and conceptual mastery. By analyzing student performance and feedback, we assess their impact on engagement, self-efficacy, and deeper mathematical understanding. The findings offer valuable insights for enhancing instructional strategies and foste…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
Regional climate risk assessment from climate models using probabilistic machine learning

EPC Framework for BESS Projects
Zeenat Hameed, Chresten Tr{\ae}holt
https://arxiv.org/abs/2506.13281 https://arxiv.org/pdf/2506.13281…

EPC Framework for BESS Projects
Battery Energy Storage Systems (BESS) are critical for modern power networks, supporting grid services such as frequency regulation, peak shaving, and black start. Delivering a BESS under an Engineering, Procurement, and Construction (EPC) model requires a concise methodology that balances regulatory compliance, technical details, and schedule efficiency. This paper presents a streamlined, five step EPC framework covering feasibility assessment, permitting, procurement, construction, and commis…

Assessment of Polarization Entanglement Source: Photon Counting and Correlation Measurement
Tom\'a\v{s} Nov\'ak, Martin Guldan, Josef Vojt\v{e}ch, Josef Bla\v{z}ej
https://arxiv.org/abs/2505.23979

Assessment of Polarization Entanglement Source: Photon Counting and Correlation Measurement
Commercial sources of polarization entanglement at telecommunication wavelengths are already available on the market, but they lack proper certification or third-party testing. We aim to provide a comprehensive testing framework for photon counting and correlation measurements to characterize the parameters of these sources in a scalable and repeatable manner. The detection setup is included in our considerations, as the non-idealities of the components negatively affect the relevance of the me…

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
Sompote Youwai, David Phim, Vianne Gayl Murcia, Rianne Clair Onas
https://arxiv.org/abs/2506.13811

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
This study investigates router-based multi-agent systems for automating foundation design calculations through intelligent task classification and expert selection. Three approaches were evaluated: single-agent processing, multi-agent designer-checker architecture, and router-based expert selection. Performance assessment utilized baseline models including DeepSeek R1, ChatGPT 4 Turbo, Grok 3, and Gemini 2.5 Pro across shallow foundation and pile design scenarios. The router-based configuration…

Developing Assessment Methods for Evaluating Learning Experience
Maneesha
https://arxiv.org/abs/2505.04176 https://arxiv.org/pdf/2505…

Developing Assessment Methods for Evaluating Learning Experience
This research aims to investigate the gender-based learning experiences of engineering students enrolled in the Probability and Statistics course, focusing on the four different assessment methods employed namely direct conceptual learning (DCL), symposium, applied deployment and collaborative learning. The study encompasses 299 engineering students, comprising 90 females and 209 males. Multivariate Analysis of Variance (MANOVA), is used to gain deeper insights into the complex interplay betwee…

Dataset of News Articles with Provenance Metadata for Media Relevance Assessment
Tomas Peterka, Matyas Bohacek
https://arxiv.org/abs/2506.09847 https://

Dataset of News Articles with Provenance Metadata for Media Relevance Assessment
Out-of-context and misattributed imagery is the leading form of media manipulation in today's misinformation and disinformation landscape. The existing methods attempting to detect this practice often only consider whether the semantics of the imagery corresponds to the text narrative, missing manipulation so long as the depicted objects or scenes somewhat correspond to the narrative at hand. To tackle this, we introduce News Media Provenance Dataset, a dataset of news articles with provenance-…

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Eliott Thomas, Mickael Coustaty, Aurelie Joseph, Gaspar Deloin, Elodie Carel, Vincent Poulain D'Andecy, Jean-Marc Ogier
https://arxiv.org/abs/2506.14568

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) can leverage unlabeled data, existing methods rely on confidence scores that poorly reflect extraction quality. We propose QUEST, a Quality-aware Semi-supervised Table extraction framework designed for business documents. QUEST introduces a novel quality assessment model that ev…

A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI
Sapolnach Prompiengchai, Charith Narreddy, Steve Joordens
https://arxiv.org/abs/2505.23405

A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI
Formative assessment is a cornerstone of effective teaching and learning, providing students with feedback to guide their learning. While there has been an exponential growth in the application of generative AI in scaling various aspects of formative assessment, ranging from automatic question generation to intelligent tutoring systems and personalized feedback, few have directly addressed the core pedagogical principles of formative assessment. Here, we critically examined how generative AI, e…

Near-real-time ship grounding damage assessment using Bayesian networks
Dimitris G. Georgiadis, Manolis S. Samuelides, Daniel Straub
https://arxiv.org/abs/2506.06493

Near-real-time ship grounding damage assessment using Bayesian networks
In a post-grounding event, the rapid assessment of hull girder residual strength is crucial for making informed decisions, such as determining whether the vessel can safely reach the closest yard. One of the primary challenges in this assessment is the uncertainty in the estimation of the extent of structural damage. Although classification societies have developed rapid response damage assessment tools, primarily relying on 2D Smith-based models, these tools are based on deterministic methods …

Realistic assessment of a single gravitational wave source localization taking into account precise pulsar distances with pulsar timing arrays
Ryo Kato, Keitaro Takahashi
https://arxiv.org/abs/2506.02819

Realistic assessment of a single gravitational wave source localization taking into account precise pulsar distances with pulsar timing arrays
Pulsar timing arrays (PTAs) are anticipated to detect continuous gravitational waves (GWs) from individual supermassive black hole binaries (SMBHBs) in the near future. To identify the host galaxy of a GW source, PTAs require significantly improved angular resolution beyond the typical range of 100-1000 square degrees achieved by recent continuous GW searches. In this study, we investigate how precise pulsar distance measurements can enhance the localization of a single GW source. Accurate dist…

🚨 🚨 🚨 "An Initial Assessment of Proportional Ranked-Choice Voting in Portland, Oregon" - Election Law Blog
https://electionlawblog.org/?p=150163

OUP hasn’t suddenly turned evil, this has been going on for years now.
Here’s another reason why not to publish in DSH.
https://www.alpsp.org/news-publications/industry-news/oxford-university-pre…

Quality assessment of 3D human animation: Subjective and objective evaluation
Rim Rekik, Stefanie Wuhrer, Ludovic Hoyet, Katja Zibrek, Anne-H\'el\`ene Olivier
https://arxiv.org/abs/2505.23301

Quality assessment of 3D human animation: Subjective and objective evaluation
Virtual human animations have a wide range of applications in virtual and augmented reality. While automatic generation methods of animated virtual humans have been developed, assessing their quality remains challenging. Recently, approaches introducing task-oriented evaluation metrics have been proposed, leveraging neural network training. However, quality assessment measures for animated virtual humans that are not generated with parametric body models have yet to be developed. In this contex…

An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Ishan Mishra, Nikole Lewis, Jonathan Lunine, Kevin P. Hand
https://arxiv.org/abs/2506.01842

An Assessment of Organics Detection and Characterization on the Surface of Europa with Infrared Spectroscopy
Organics, if they do exist on Europa, may only be present in trace amounts on the surface. NASA's upcoming mission Europa Clipper is going to provide global, high quality data of the surface of Europa in the near-infrared (NIR), specifically the 3-5~$μ$m region, where organics are rich in spectroscopic features. In this work we investigate Europa Clipper's ability to constrain the abundance of selected trace species of interest that span different chemical bonds found in organics, such as C-H,…

Data Verbalisation: What is Text Doing in a Data Visualisation?
Paul Murrell
https://arxiv.org/abs/2506.15129 https://arxiv.org/pdf/2…

Data Verbalisation: What is Text Doing in a Data Visualisation?
This article discusses the role that text elements play in a data visualisation. We argue that there is a need for a simple, coherent explanation of text elements similar to the understanding that already exists for non-text elements like bars, points, and lines. We explore examples of how text is used within a data visualisation and use existing knowledge and assessment techniques to evaluate when text is effective and when it is not. The result is a framework that aims to be easy to understan…

Transform-Resampled Double Bootstrap Percentile with Applications in System Reliability Assessment
Junpeng Gong, Xu He, Zhaohui Li
https://arxiv.org/abs/2506.04573

Transform-Resampled Double Bootstrap Percentile with Applications in System Reliability Assessment
System reliability assessment(SRA) is a challenging task due to the limited experimental data and the complex nature of the system structures. Despite a long history dating back to \cite{buehler1957confidence}, exact methods have only been applied to SRA for simple systems. High-order asymptotic methods, such as the Cornish-Fisher expansion, have become popular for balancing computational efficiency with improved accuracy when data are limited, but frequently encounter the "bend-back" problem i…

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2506.14224

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration of their internal mechanisms. In response, this study adopts an approach based on internal mechanisms to provide an interpretability-driven assessment of ToM in multimodal large …

Met mijn hart gaat het goed.
Maar het gat in mijn lies(slagader) is vanmorgen gaan bloeden en opnieuw verbonden met een modern drukpleistersysteem (https://www.merit.com/product/safeguard-pressure-assisted-device/)
Nu mag ik zes uur stil op mijn ru…

SafeGuard® Pressure Assisted Device
Safeguard pressure assisted device maintains consistent pressure on the access site and facilitates site assessment through the clear window.

Early Assessment of Artificial Lower Extremity Sensory Response Times and Proprioceptive Acuity via Sensory Cortex Electrical Stimulation
Won Joon Sohn, Jeffrey Lim, Po T. Wang, Susan J. Shaw, Michelle Armacost, Hui Gong, Brian Lee, Darrin Lee, Payam Heydari, Richard A. Andersen, Charles Y. Liu, Zoran Nenadic, An H. Do
https://a…

Early Assessment of Artificial Lower Extremity Sensory Response Times and Proprioceptive Acuity via Sensory Cortex Electrical Stimulation
Bi-directional brain computer interfaces (BD-BCIs) may restore brain-controlled walking and artificial leg sensation after spinal cord injury. Current BD-BCIs provide only simplistic "tingling" feedback, which lacks proprioceptive information to perceive critical gait events (leg swing, double support). This information must also be perceived adequately fast to facilitate timely motor responses. Here, we investigated utilizing primary sensory cortex (S1) direct cortical electrical stimulation (…

Join the 2025 Maine AI 'unconference' on June 13 in Orono for face-to-face discussions whose topics will be democratically chosen by the attenders. (We've got expert panels too.) Register by this Friday—more at https://www.linkedin.com/posts/jonippolito

'Active travel' study identifies pathways for designing walking- and cycling-friendly cities #city

'Active travel' study identifies pathways for designing walking- and cycling-friendly cities
A new study published in the Proceedings of the National Academy of Sciences reveals the transformative potential of walking- and cycling-friendly policies in cities around the world. Conducted by researchers from UCLA and Google, the study analyzed travel data from more than 11,500 cities across 121 countries, making it the most comprehensive global assessment to date of active transportation, or the potential for walking or biking.

Framework of a multiscale data-driven digital twin of the muscle-skeletal system
Martina Paccini, Simone Cammarasana, Giuseppe Patan\`e
https://arxiv.org/abs/2506.11821

Framework of a multiscale data-driven digital twin of the muscle-skeletal system
Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, requiring advanced diagnostic and therapeutic tools for personalised assessment and treatment. Effective management of MSDs involves the interaction of heterogeneous data sources, making the Digital Twin (DT) paradigm a valuable option. This paper introduces the Musculoskeletal Digital Twin (MS-DT), a novel framework that integrates multiscale biomechanical data with computational modelling to create a detailed, patie…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
NeRF-QA: Neural Radiance Fields Quality Assessment Database
https://

This https://arxiv.org/abs/2505.23405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI
Formative assessment is a cornerstone of effective teaching and learning, providing students with feedback to guide their learning. While there has been an exponential growth in the application of generative AI in scaling various aspects of formative assessment, ranging from automatic question generation to intelligent tutoring systems and personalized feedback, few have directly addressed the core pedagogical principles of formative assessment. Here, we critically examined how generative AI, e…

Detection, attribution, and modeling of climate change: key open issues
Nicola Scafetta
https://arxiv.org/abs/2506.13994 https://arxi…

Detection, attribution, and modeling of climate change: key open issues
The CMIP global climate models (GCMs) assess that nearly 100% of global surface warming observed between 1850-1900 and 2011-2020 is attributable to anthropogenic drivers like greenhouse gas emissions. These models also generate future climate projections based on shared socioeconomic pathways (SSPs), aiding in risk assessment and the development of costly Net-Zero climate mitigation strategies. Yet, the CMIP GCMs face significant scientific challenges in attributing and modeling climate change,…

Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
Stefan Orf, Sven Ochs, Marc Ren\'e Zofka, J. Marius Z\"ollner
https://arxiv.org/abs/2506.02932

Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the utilization of high-definition maps, prediction of other road participants and the controlling of the vehicle itself. Due to this reason, the localization is absolutely safety relevant. Typical errors of the localization systems, which are long term drifts, jumps and false localization, that must be detected to enhance s…

This https://arxiv.org/abs/2506.02976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO AMD Progression Challenge
The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical coherence tomography (OCT) images. Designed to evaluate algorithmic performance in detecting neovascular activity changes within AMD, the challenge incorporated unique multi-modal datasets. The primary dataset, sourced from Brest, France, was used by participating teams to train and test their models. The final ranking wa…

Trump's top general contradicts his assessment of Putin, L.A. unrest (Washington Post)
https://www.washingtonpost.com/national-security/2025/06/11/dan-caine-trump-la-protests-russia/
http://www.memeorandum.com/250611/p140#a250611p140

LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Songze Li, Chuokun Xu, Jiaying Wang, Xueluan Gong, Chen Chen, Jirui Zhang, Jun Wang, Kwok-Yan Lam, Shouling Ji
https://arxiv.org/abs/2506.09443

LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Large Language Models (LLMs) have demonstrated remarkable intelligence across various tasks, which has inspired the development and widespread adoption of LLM-as-a-Judge systems for automated model testing, such as red teaming and benchmarking. However, these systems are susceptible to adversarial attacks that can manipulate evaluation outcomes, raising concerns about their robustness and, consequently, their trustworthiness. Existing evaluation methods adopted by LLM-based judges are often pie…

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
Hongda Zhu, Yiwen Zhang, Bing Zhao, Jingzhe Ding, Siyao Liu, Tong Liu, Dandan Wang, Yanan Liu, Zhaojian Li
https://arxiv.org/abs/2506.13832

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
Large Language Models (LLMs) have made significant strides in front-end code generation. However, existing benchmarks exhibit several critical limitations: many tasks are overly simplistic, test cases often lack rigor, and end-to-end validation is absent. These issues hinder the accurate assessment of model performance. To address these challenges, we present FrontendBench, a benchmark co-developed by humans and LLMs. FrontendBench categorizes tasks based on code functionality and incorporates …

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
Taozhi Guo, Gumaro Rendon, Rutuja Kshirsagar
https://arxiv.org/abs/2506.09318

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
In this work, we present a Gibbs state observable estimation algorithm based on Trotter interpolation, which reaches a state-of-the-art quantum computational cost of $ \tilde{O}(β\log{1/ε})$. Our approach saves $\log(Γ)$ ancilla qubits compared with the qubitization-based methods for Hamiltonian with $Γ$ stages. To provide a robust assessment of our approach, we benchmark our results against state-of-the-art methodology using the SYK model as a testbed. Our method provides an efficient alte…

Universal Preference-Score-based Pairwise Speech Quality Assessment
Yu-Fei Shi, Yang Ai, Zhen-Hua Ling
https://arxiv.org/abs/2506.01455 https://

Universal Preference-Score-based Pairwise Speech Quality Assessment
To compare the performance of two speech generation sys- tems, one of the most effective approaches is estimating the preference score between their generated speech. This pa- per proposes a novel universal preference-score-based pairwise speech quality assessment (UPPSQA) model, aimed at predict- ing the preference score between paired speech samples to de- termine which one has better quality. The model first predicts the absolute mean opinion score (MOS) for the two speech sam- ples separate…

A novel stratified sampler with unbalanced refinement for network reliability assessment
Jianpeng Chan, Iason Papaioannou, Daniel Straub
https://arxiv.org/abs/2506.01044

A novel stratified sampler with unbalanced refinement for network reliability assessment
We investigate stratified sampling in the context of network reliability assessment. We propose an unbalanced stratum refinement procedure, which operates on a partition of network components into clusters and the number of failed components within each cluster. The size of each refined stratum and the associated conditional failure probability, collectively termed failure signatures, can be calculated and estimated using the conditional Bernoulli model. The estimator is further improved by det…

Evaluating Logit-Based GOP Scores for Mispronunciation Detection
Aditya Kamlesh Parikh, Cristian Tejedor-Garcia, Catia Cucchiarini, Helmer Strik
https://arxiv.org/abs/2506.12067

Evaluating Logit-Based GOP Scores for Mispronunciation Detection
Pronunciation assessment relies on goodness of pronunciation (GOP) scores, traditionally derived from softmax-based posterior probabilities. However, posterior probabilities may suffer from overconfidence and poor phoneme separation, limiting their effectiveness. This study compares logit-based GOP scores with probability-based GOP scores for mispronunciation detection. We conducted our experiment on two L2 English speech datasets spoken by Dutch and Mandarin speakers, assessing classification …

Probabilistic Assessment of Engineered Timber Reusability after Moisture Exposure
Yiping Meng, Chulin Jiang, Courtney Jayne Scurr, Farzad Pour Rahimian, David Hughes
https://arxiv.org/abs/2506.11061

Probabilistic Assessment of Engineered Timber Reusability after Moisture Exposure
Engineered timber is pivotal to low-carbon construction, but moisture uptake during its service life can compromise structural reliability and impede reuse within a circular economy model. Despite growing interest, quantitative standards for classifying the reusability of moisture-exposed timber are still lacking. This study develops a probabilistic framework to determine the post-exposure reusability of engineered timber. Laminated specimens were soaked to full saturation, dried to 25% moistur…

Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models
Gon\c{c}alo Mesquita, Ana Rita C\'oias, Artur Dubrawski, Alexandre Bernardino
https://arxiv.org/abs/2506.03752

Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models
The growing demands of stroke rehabilitation have increased the need for solutions to support autonomous exercising. Virtual coaches can provide real-time exercise feedback from video data, helping patients improve motor function and keep engagement. However, training real-time motion analysis systems demands frame-level annotations, which are time-consuming and costly to obtain. In this work, we present a framework that learns to classify individual frames from video-level annotations for real…

BacPrep: An Experimental Platform for Evaluating LLM-Based Bacalaureat Assessment
Dumitran Adrian Marius, Dita Radu
https://arxiv.org/abs/2506.04989 https:…

BacPrep: An Experimental Platform for Evaluating LLM-Based Bacalaureat Assessment
Accessing quality preparation and feedback for the Romanian Bacalaureat exam is challenging, particularly for students in remote or underserved areas. This paper introduces BacPrep, an experimental online platform exploring Large Language Model (LLM) potential for automated assessment, aiming to offer a free, accessible resource. Using official exam questions from the last 5 years, BacPrep employs one of Google's newest models, Gemini 2.0 Flash (released Feb 2025), guided by official grading sc…

Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG
Zarreen Reza, Alexander Mazur, Michael T. Dugdale, Robin Ray-Chaudhuri
https://arxiv.org/abs/2506.05925

Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG
While Large Language Models (LLMs) are increasingly utilized as student-facing educational aids, their potential to directly support educators, particularly through locally deployable and customizable open-source solutions, remains significantly underexplored. Many existing educational solutions rely on cloud-based infrastructure or proprietary tools, which are costly and may raise privacy concerns. Regulated industries with limited budgets require affordable, self-hosted solutions. We introduc…

This https://arxiv.org/abs/2505.17371 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

An End-to-End Approach for Child Reading Assessment in the Xhosa Language
Child literacy is a strong predictor of life outcomes at the subsequent stages of an individual's life. This points to a need for targeted interventions in vulnerable low and middle income populations to help bridge the gap between literacy levels in these regions and high income ones. In this effort, reading assessments provide an important tool to measure the effectiveness of these programs and AI can be a reliable and economical tool to support educators with this task. Developing accurate a…

ATAG: AI-Agent Application Threat Assessment with Attack Graphs
Parth Atulbhai Gandhi, Akansha Shukla, David Tayouri, Beni Ifland, Yuval Elovici, Rami Puzis, Asaf Shabtai
https://arxiv.org/abs/2506.02859

ATAG: AI-Agent Application Threat Assessment with Attack Graphs
Evaluating the security of multi-agent systems (MASs) powered by large language models (LLMs) is challenging, primarily because of the systems' complex internal dynamics and the evolving nature of LLM vulnerabilities. Traditional attack graph (AG) methods often lack the specific capabilities to model attacks on LLMs. This paper introduces AI-agent application Threat assessment with Attack Graphs (ATAG), a novel framework designed to systematically analyze the security risks associated with AI-a…

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
Christian Reichenb\"acher, Philipp Rank, Jochen Hipp, Oliver Bringmann
https://arxiv.org/abs/2506.10098

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
This paper presents the first application of Gaussian Mixture Copula Models to the statistical modeling of driving scenarios for the safety validation of automated driving systems. Knowledge of the joint probability distribution of scenario parameters is essential for scenario-based safety assessment, where risk quantification depends on the likelihood of concrete parameter combinations. Gaussian Mixture Copula Models bring together the multimodal expressivity of Gaussian Mixture Models and the…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new/
[4/4]:
SALAD: Systematic Assessment of Machine Unlearing on LLM-Aided Hardware Design

Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment
Canh Thien Dang, An Nguyen
https://arxiv.org/abs/2506.00050

Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment
As artificial intelligence (AI) becomes integral to the society, the ability to critically evaluate AI-generated content is increasingly vital. On the context of management education, we examine how academic skills, cognitive traits, and AI scepticism influence students' ability to detect factually incorrect AI-generated responses (hallucinations) in a high-stakes assessment at a UK business school (n=211, Year 2 economics and management students). We find that only 20% successfully identified …

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
https://arxiv.org/abs/2506.05990

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated o…

Accurate analysis of the pitch pulse-based magnitude/phase structure of natural vowels and assessment of three lightweight time/frequency voicing restoration methods
An\'ibal J. S. Ferreira, Luis M. T. Jesus, Laurentino M. M. Leal, Jorge E. F. Spratley
https://arxiv.org/abs/2506.06675

Accurate analysis of the pitch pulse-based magnitude/phase structure of natural vowels and assessment of three lightweight time/frequency voicing restoration methods
Whispered speech is produced when the vocal folds are not used, either intentionally, or due to a temporary or permanent voice condition. The essential difference between natural speech and whispered speech is that periodic signal components that exist in certain regions of the former, called voiced regions, as a consequence of the vibration of the vocal folds, are missing in the latter. The restoration of natural speech from whispered speech requires delicate signal processing procedures that …

Human Empathy as Encoder: AI-Assisted Depression Assessment in Special Education
Boning Zhao
https://arxiv.org/abs/2505.23631 https://

Human Empathy as Encoder: AI-Assisted Depression Assessment in Special Education
Assessing student depression in sensitive environments like special education is challenging. Standardized questionnaires may not fully reflect students' true situations. Furthermore, automated methods often falter with rich student narratives, lacking the crucial, individualized insights stemming from teachers' empathetic connections with students. Existing methods often fail to address this ambiguity or effectively integrate educator understanding. To address these limitations by fostering a …

Towards Lean Research Inception: Assessing Practical Relevance of Formulated Research Problems
Anrafel Fernandes Pereira, Marcos Kalinowski, Maria Teresa Baldassarre, J\"urgen B\"orstler, Nauman bin Ali, Daniel Mendez
https://arxiv.org/abs/2506.12669

Towards Lean Research Inception: Assessing Practical Relevance of Formulated Research Problems
[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry connections, and poorly defined research problems. Clear criteria for evaluating SE research problems can help align their value, feasibility, and applicability with industrial needs. [Goal] In this paper, we introduce the Lean Research Inception (LRI) framework, designed to support the formulation and assessment of prac…

This https://arxiv.org/abs/2505.21356 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Towards Robust Automated Perceptual Voice Quality Assessment with Deep Learning
Objective: Perceptual voice quality assessment plays a critical role in diagnosing and monitoring voice disorders by providing standardized evaluation of vocal function. Traditionally, this process relies on expert raters utilizing standard scales, such as the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) and Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS). However, these metrics are inherently subjective and susceptible to inter-rater variability, motivating the need f…

Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning
Mahdi Bashiri Bawil, Mousa Shamsi, Abolhassan Shakeri Bavil
https://arxiv.org/abs/2506.07123

Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning
Multiple sclerosis (MS) diagnosis and monitoring rely heavily on accurate assessment of brain MRI biomarkers, particularly white matter hyperintensities (WMHs) and ventricular changes. Current segmentation approaches suffer from several limitations: they typically segment these structures independently despite their pathophysiological relationship, struggle to differentiate between normal and pathological hyperintensities, and are poorly optimized for anisotropic clinical MRI data. We propose a…

Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Weibo Gao, Qi Liu, Rui Li, Yuze Zhao, Hao Wang, Linan Yre, Fangzhou Yao, Zheng Zhang
https://arxiv.org/abs/2506.11107

Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective and personalized programming education. However, current PKT studies primarily focus on the implicit relationship between code content and knowledge assessment, often overlooking two types of noise signals in long-term programming activities: unwanted signals from unrelated submissions and weak signals from minor modifi…

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
David Farr, Kevin Talty, Alexandra Farr, John Stockdale, Iain Cruickshank, Jevin West
https://arxiv.org/abs/2506.10104

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weakness Enumerations (CWEs), comparing zero-shot, few-shot cross-domain, and few-shot in-domain prompting strategies. Our results indicate that while zero-shot prompting performs poorly, few-shot prompti…

EMG-Driven Stiffness-Modulating Palpation for Telerehabilitation
Thomas M. Kwok, Hilary HY Cheng, Wai Tuck Chow
https://arxiv.org/abs/2506.08303 https://…

EMG-Driven Stiffness-Modulating Palpation for Telerehabilitation
In this work, we introduce HJ-Pal, a lightweight wearable haptic device that leverages EMG-driven honeycomb jamming to render muscle activation as kinesthetic feedback, enabling remote palpation for small muscle assessment in telerehabilitation.

WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
Liangliang Chen, Huiru Xie, Jacqueline Rohde, Ying Zhang
https://arxiv.org/abs/2506.08962

WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
This research-to-practice work-in-progress (WIP) paper presents an AI-enabled smart tutor designed to provide homework assessment and feedback for students in an undergraduate circuit analysis course. We detail the tutor's design philosophy and core components, including open-ended question answering and homework feedback generation. The prompts are carefully crafted to optimize responses across different problems. The smart tutor was deployed on the Microsoft Azure platform and is currently in…

This https://arxiv.org/abs/2506.03507 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Software Bill of Materials in Software Supply Chain Security A Systematic Literature Review
Software Bill of Materials (SBOMs) are increasingly regarded as essential tools for securing software supply chains (SSCs), yet their real-world use and adoption barriers remain poorly understood. This systematic literature review synthesizes evidence from 40 peer-reviewed studies to evaluate how SBOMs are currently used to bolster SSC security. We identify five primary application areas: vulnerability management, transparency, component assessment, risk assessment, and SSC integrity. Despite c…

Software Bill of Materials in Software Supply Chain Security A Systematic Literature Review
Eric O'Donoghue, Yvette Hastings, Ernesto Ortiz, A. Redempta Manzi Muneza
https://arxiv.org/abs/2506.03507

Software Bill of Materials in Software Supply Chain Security A Systematic Literature Review
Software Bill of Materials (SBOMs) are increasingly regarded as essential tools for securing software supply chains (SSCs), yet their real-world use and adoption barriers remain poorly understood. This systematic literature review synthesizes evidence from 40 peer-reviewed studies to evaluate how SBOMs are currently used to bolster SSC security. We identify five primary application areas: vulnerability management, transparency, component assessment, risk assessment, and SSC integrity. Despite c…

An Accurate and Efficient Vulnerability Propagation Analysis Framework
Bonan Ruan, Zhiwei Lin, Jiahao Liu, Chuqi Zhang, Kaihang Ji, Zhenkai Liang
https://arxiv.org/abs/2506.01342 …

An Accurate and Efficient Vulnerability Propagation Analysis Framework
Identifying the impact scope and scale is critical for software supply chain vulnerability assessment. However, existing studies face substantial limitations. First, prior studies either work at coarse package-level granularity, producing many false positives, or fail to accomplish whole-ecosystem vulnerability propagation analysis. Second, although vulnerability assessment indicators like CVSS characterize individual vulnerabilities, no metric exists to specifically quantify the dynamic impact…

AutoGEEval : A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine
Shuyang Hou, Zhangxiao Shen, Huayi Wu, Haoyue Jiao, Ziqi Liu, Lutong Xie, Chang Liu, Jianyuan Liang, Yaxian Qing, Xiaopu Zhang, Dehua Peng, Zhipeng Gui, Xuefeng Guan
https://

AutoGEEval++: A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine
Geospatial code generation is becoming a key frontier in integrating artificial intelligence with geo-scientific analysis, yet standardised automated evaluation tools for this task remain absent. This study presents AutoGEEval++, an enhanced framework building on AutoGEEval, and the first automated assessment system for large language models (LLMs) generating geospatial code on Google Earth Engine (GEE). It supports diverse data modalities and varying task complexities. Built on the GEE Python …