mstyslavity/prisma_v3 huggingface dataset:
In Nigeria, electoral behavior is often interpreted through ethno-religious views and regional allegiances, without empirically assessing the influence of socioeconomic indicators such as health, income, education, and other deprivations on voter behavior. This study investigates the voting pattern in the 2023 Nigerian presidential election and previous cycles using spatio-temporal and multivariate analysis. It examines whether support for some candidates was more substantial in states with higher Human Development Index (HDI) and whether this alignment impacts governance quality and macroeconomic performance post-election. Socioeconomic data were obtained from the Global Data Lab and the Nigerian Bureau of Statistics (NBS) for the year preceding each election, while presidential vote results were sourced from the Independent National Electoral Commission (INEC). The results show that the Labour Party (LP) dominated states with high socioeconomic indices, accounting for about 30-53% of the variance in voting patterns of LP and People's Democratic Party (PDP), while the All Progressive Congress (APC) variance is less explained by these factors. Multinomial logits based on HDI is used to predict party win probabilities  the model predicted about 60% wins accurately. Comparative analysis of four presidential cycles revealed that in 2011, the winner had an HDI-vote correlation of 0.44, with improved macroeconomic indices post-election. In contrast, 2015, 2019, and 2023 saw negative correlations of -0.38, -0.43, and -0.34, respectively, alongside macroeconomic decline. The findings suggest that socioeconomic development shapes political preferences, promotes issue-based politics, and supports quality leadership  therefore, strengthening education, healthcare, and poverty reduction should be prioritized to enhance citizens' well-being and build an informed electorate.
Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, ``Oracle'' (without explainability) versus ``AI Colleague'' (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices.
Consistent high-quality nursing care is essential for patient safety, yet current nursing education depends on subjective, time-intensive instructor feedback in training future nurses, which limits scalability and efficiency in their training, and thus hampers nursing competency when they enter the workforce. In this paper, we introduce a video-language model (VLM) based framework to develop the AI capability of automated procedural assessment and feedback for nursing skills training, with the potential of being integrated into existing training programs. Mimicking human skill acquisition, the framework follows a curriculum-inspired progression, advancing from high-level action recognition, fine-grained subaction decomposition, and ultimately to procedural reasoning. This design supports scalable evaluation by reducing instructor workload while preserving assessment quality. The system provides three core capabilities: 1) diagnosing errors by identifying missing or incorrect subactions in nursing skill instruction videos, 2) generating explainable feedback by clarifying why a step is out of order or omitted, and 3) enabling objective, consistent formative evaluation of procedures. Validation on synthesized videos demonstrates reliable error detection and temporal localization, confirming its potential to handle real-world training variability. By addressing workflow bottlenecks and supporting large-scale, standardized evaluation, this work advances AI applications in nursing education, contributing to stronger workforce development and ultimately safer patient care.
Visual Question Answering (VQA) is increasingly used in diverse applications ranging from general visual reasoning to safety-critical domains such as medical imaging and autonomous systems, where models must provide not only accurate answers but also explanations that humans can easily understand and verify. Prototype-based modeling has shown promise for interpretability by grounding predictions in semantically meaningful regions for purely visual reasoning tasks, yet remains underexplored in the context of VQA. We present ProtoVQA, a unified prototypical framework that (i) learns question-aware prototypes that serve as reasoning anchors, connecting answers to discriminative image regions, (ii) applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant, and (iii) supports both answering and grounding tasks through a shared prototype backbone. To assess explanation quality, we propose the Visual-Linguistic Alignment Score (VLAS), which measures how well the model's attended regions align with ground-truth evidence. Experiments on Visual7W show that ProtoVQA yields faithful, fine-grained explanations while maintaining competitive accuracy, advancing the development of transparent and trustworthy VQA systems.
Large language models (LLMs) have demonstrated promising performance on\nmedical benchmarks; however, their ability to perform medical calculations, a\ncrucial aspect of clinical decision-making, remains underexplored and poorly\nevaluated. Existing benchmarks often assess only the final answer with a wide\nnumerical tolerance, overlooking systematic reasoning failures and potentially\ncausing serious clinical misjudgments. In this work, we revisit medical\ncalculation evaluation with a stronger focus on clinical trustworthiness.\nFirst, we clean and restructure the MedCalc-Bench dataset and propose a new\nstep-by-step evaluation pipeline that independently assesses formula selection,\nentity extraction, and arithmetic computation. Under this granular framework,\nthe accuracy of GPT-4o drops from 62.7% to 43.6%, revealing errors masked by\nprior evaluations. Second, we introduce an automatic error analysis framework\nthat generates structured attribution for each failure mode. Human evaluation\nconfirms its alignment with expert judgment, enabling scalable and explainable\ndiagnostics. Finally, we propose a modular agentic pipeline, MedRaC, that\ncombines retrieval-augmented generation and Python-based code execution.\nWithout any fine-tuning, MedRaC improves the accuracy of different LLMs from\n16.35% up to 53.19%. Our work highlights the limitations of current benchmark\npractices and proposes a more clinically faithful methodology. By enabling\ntransparent and transferable reasoning evaluation, we move closer to making\nLLM-based systems trustworthy for real-world medical applications"""
Recent black-box counterfactual generation frameworks fail to take into account the semantic content of the proposed edits, while relying heavily on training to guide the generation process. We propose a novel, plug-and-play black-box counterfactual generation framework, which suggests step-by-step edits based on theoretical guarantees of optimal edits to produce human-level counterfactual explanations with zero training. Our framework utilizes a pre-trained image editing diffusion model, and operates without access to the internals of the classifier, leading to an explainable counterfactual generation process. Throughout our experimentation, we showcase the explanatory gap between human reasoning and neural model behavior by utilizing both Convolutional Neural Network (CNN), Vision Transformer (ViT) and Large Vision Language Model (LVLM) classifiers, substantiated through a comprehensive human evaluation.
Trust is central to human social interactions, manifesting in actions that make one vulnerable to another. We argue that trust will thus depend on the decision-making processes that arise in neural systems. Building on advances in the cognitive neuroscience of decision making, we propose a mechanistic model of trust arising from multiple parallel systems that perform distinct, complementary information processing. Because each system learns via different mechanisms, trust can be created (or destroyed) in multiple ways. This systems-level taxonomy of information representations provides a principled basis for differentiating forms of trust, linking them to specific learning processes, and generating testable predictions about their expression in behavior. By situating trust within a broader theory of neural decision systems, our account unifies diverse findings across psychology, neuroscience, and the social sciences, and offers a foundation for explaining how humans develop, maintain, and repair trust in a complex social world.
Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-based operational modes -- remote supervision (RSM) and remote control (RCM) -- and identify where human unsafe control actions (Human-UCAs) concentrate in handover and emergency loops; (ii) summarize evidence that transparency features (decision rationales, alternatives, confidence/uncertainty, and rule-compliance indicators) improve understanding and support trust calibration, though reliability and predictability often dominate trust; (iii) distill design strategies for transparency at three layers: sensor/SA acquisition and fusion, HMI/eHMI presentation (textual/graphical overlays, color coding, conversational and immersive UIs), and engineer-facing processes (resilient interaction design, validation, and standardization). We integrate methods for Human-UCA identification (STPA-Cog + IDAC), quantitative trust/SA assessment, and operator workload monitoring, and outline regulatory and rule-based implications including COLREGs formalization and route exchange. We conclude with an adaptive transparency framework that couples operator state estimation with explainable decision support to reduce cognitive overload and improve takeover timeliness. The review highlights actionable figure-of-merit displays (e.g., CPA/TCPA risk bars, robustness heatmaps), transparent model outputs (rule traceability, confidence), and training pipelines (HIL/MIL, simulation) as near-term levers for safer MASS operations.
Understanding how built environments shape human experience is central to designing sustainable cities. Cycling provides a critical case: it delivers health and environmental benefits, yet its uptake depends strongly on the experience of cycling rather than infrastructure alone. Research on this relationship has grown rapidly but remains fragmented across disciplines and scales, and has concentrated on network-level analyses of routes and connectivity. This bias is especially problematic in historical cities, where embedding new infrastructure is difficult, and where cycling experience is shaped not only by spatial form but also by how cyclists perceive, interpret, and physically respond to their environment - through psychological factors such as safety and comfort, physiological demands such as stress and fatigue, and perceptual cues in the streetscape. We systematically reviewed 68 studies across urban planning, transportation, behavioural science, neuroscience, and public health. Two scales of analysis were identified: a macro scale addressing the ability to cycle and a micro scale addressing the propensity to cycle. Methods were classified into objective and subjective approaches, with hybrid approaches beginning to emerge. We find a persistent reliance on objective proxies, limited integration of subjective accounts, and insufficient attention to the streetscape as a lived environment. Addressing these gaps is essential to explain why environments enable or deter cycling, and to inform the design of cities that support cycling as both mobility and lived experience.
The diffusion of micro- and nano-swimmers in a fluid, confined within irregular structures that impose entropic barriers, is often modeled using overdamped active Brownian dynamics, where viscous effects are paramount and inertia is negligible. Here, we numerically investigate the diffusive behavior of chiral self-propelled particles in a two-dimensional asymmetric channel subjected to an external torque arising from a gravitational field. We reveal the emergence of resonant diffusion when the external torque $\omega$ approaches the intrinsic angular velocity $\omega_{0}$ of particles. This resonance manifests as a pronounced accumulation of particles near the upper-left corner of the channel, accompanied by an enhanced peak in the effective diffusion coefficient. In particular, it is observed only for low rotational diffusion rates and does not persist beyond moderate values of $\omega_{0}$. Prominent transport features, such as rectification at low values of $\omega$, a monotonic increase in average velocity with $\omega$, and a nonmonotonic response of transport characteristics (average velocity and effective diffusion coefficient) as a function of the rotational diffusion rate near the resonance point, are explained. Furthermore, we show that the transport characteristics depend strongly on the aspect ratio of the channel. For instance, the enhanced diffusion peak becomes more pronounced with increasing aspect ratio, and the average velocity saturates at higher values for wider bottleneck openings. It is conceivable that these findings have a great potential for developing microfluidic and lab-on-a-chip devices for particle separation, targeted drug delivery, and advanced active materials.
WARNING: This paper contains examples of offensive materials. Toxic content has become pervasive on social media platforms. We introduce SMARTER, a data-efficient two-stage framework for explainable content moderation using Large Language Models (LLMs). In Stage 1, we leverage LLMs' own outputs to generate synthetic explanations for both correct and incorrect labels, enabling alignment via preference optimization with minimal human supervision. In Stage 2, we refine explanation quality through cross-model training, allowing weaker models to align stylistically and semantically with stronger ones. Experiments on three benchmark tasks -- HateXplain, Latent Hate, and Implicit Hate -- demonstrate that SMARTER enables LLMs to achieve up to a 13.5% macro-F1 improvement over standard few-shot baselines while using only a fraction of the full training data. Our framework offers a scalable strategy for low-resource settings by harnessing LLMs' self-improving capabilities for both classification and explanation.
As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To support the user-centered integration of XAI, we propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability. Our aim is to foster awareness and guide the development of user-centric XAI systems tailored to the needs of seafarers and maritime teams.
With the increasing prevalence of synthetic images, evaluating image authenticity and locating forgeries accurately while maintaining human interpretability remains a challenging task. Existing detection models primarily focus on simple authenticity classification, ultimately providing only a forgery probability or binary judgment, which offers limited explanatory insights into image authenticity. Moreover, while MLLM-based detection methods can provide more interpretable results, they still lag behind expert models in terms of pure authenticity classification accuracy. To address this, we propose DF-LLaVA, a simple yet effective framework that unlocks the intrinsic discrimination potential of MLLMs. Our approach first extracts latent knowledge from MLLMs and then injects it into training via prompts. This framework allows LLaVA to achieve outstanding detection accuracy exceeding expert models while still maintaining the interpretability offered by MLLMs. Extensive experiments confirm the superiority of our DF-LLaVA, achieving both high accuracy and explainability in synthetic image detection. Code is available online at: https://github.com/Eliot-Shen/DF-LLaVA.
Reasoning is central to purposeful action, yet most robotic foundation models map perception and instructions directly to control, which limits adaptability, generalization, and semantic grounding. We introduce Action Reasoning Models (ARMs), a class of robotic foundation models that integrate perception, planning, and control through a structured three-stage pipeline. Our model, MolmoAct, encodes observations and instructions into depth-aware perception tokens, generates mid-level spatial plans as editable trajectory traces, and predicts precise low-level actions, enabling explainable and steerable behavior. MolmoAct-7B-D achieves strong performance across simulation and real-world settings: 70.5% zero-shot accuracy on SimplerEnv Visual Matching tasks, surpassing closed-source Pi-0 and GR00T N1.5; 86.6% average success on LIBERO, including an additional 6.3% gain over ThinkAct on long-horizon tasks; and in real-world fine-tuning, an additional 10% (single-arm) and an additional 22.7% (bimanual) task progression over Pi-0-FAST. It also outperforms baselines by an additional 23.3% on out-of-distribution generalization and achieves top human-preference scores for open-ended instruction following and trajectory steering. Furthermore, we release, for the first time, the MolmoAct Dataset -- a mid-training robot dataset comprising over 10,000 high quality robot trajectories across diverse scenarios and tasks. Training with this dataset yields an average 5.5% improvement in general performance over the base model. We release all model weights, training code, our collected dataset, and our action reasoning dataset, establishing MolmoAct as both a state-of-the-art robotics foundation model and an open blueprint for building ARMs that transform perception into purposeful action through structured reasoning. Blogpost: https://allenai.org/blog/molmoact
With the rapid development of Internet technologies, web systems have become essential infrastructures for modern information exchange and business operations. However, alongside their expansion, numerous security vulnerabilities have emerged, making web security a critical research focus within the broader field of cybersecurity. These issues are closely related to data protection, privacy preservation, and business continuity, and systematic research on web security is crucial for mitigating malicious attacks and enhancing the reliability and robustness of network systems. This paper first reviews the OWASP Top 10, summarizing the types, causes, and impacts of common web vulnerabilities, and illustrates their exploitation mechanisms through representative cases. Building upon this, the Gruyere platform is adopted as an experimental subject for analyzing known vulnerabilities. The study presents detailed reproduction steps for specific vulnerabilities, proposes comprehensive remediation strategies, and further compares Gruyere's vulnerabilities with contemporary real-world cases. The findings suggest that, although Gruyere's vulnerabilities are relatively outdated, their underlying principles remain highly relevant for explaining a wide range of modern security flaws. Overall, this research demonstrates that web system security analysis based on Gruyere not only deepens the understanding of vulnerability mechanisms but also provides practical support for technological innovation and security defense.
Effort estimation is a crucial activity in agile software development, where teams collaboratively review, discuss, and estimate the effort required to complete user stories in a product backlog. Current practices in agile effort estimation heavily rely on subjective assessments, leading to inaccuracies and inconsistencies in the estimates. While recent machine learning-based methods show promising accuracy, they cannot explain or justify their estimates and lack the capability to interact with human team members. Our paper fills this significant gap by leveraging the powerful capabilities of Large Language Models (LLMs). We propose a novel LLM-based multi-agent framework for agile estimation that not only can produce estimates, but also can coordinate, communicate and discuss with human developers and other agents to reach a consensus. Evaluation results on a real-life dataset show that our approach outperforms state-of-the-art techniques across all evaluation metrics in the majority of the cases. Our human study with software development practitioners also demonstrates an overwhelmingly positive experience in collaborating with our agents in agile effort estimation.
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, and most public datasets contain no clinical reasoning or interpretation beyond image-level labels. In this paper, we present a novel method that integrates graph representation learning with vision-language models (VLMs) to deliver explainable DR diagnosis. Our approach leverages optical coherence tomography angiography (OCTA) images by constructing biologically informed graphs that encode key retinal vascular features such as vessel morphology and spatial connectivity. A graph neural network (GNN) then performs DR staging while integrated gradients highlight critical nodes and edges and their individual features that drive the classification decisions. We collect this graph-based knowledge which attributes the model's prediction to physiological structures and their characteristics. We then transform it into textual descriptions for VLMs. We perform instruction-tuning with these textual descriptions and the corresponding image to train a student VLM. This final agent can classify the disease and explain its decision in a human interpretable way solely based on a single image input. Experimental evaluations on both proprietary and public datasets demonstrate that our method not only improves classification accuracy but also offers more clinically interpretable results. An expert study further demonstrates that our method provides more accurate diagnostic explanations and paves the way for precise localization of pathologies in OCTA images.