mstyslavity/prisma_v3 huggingface dataset:
Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce patient recovery time and enhance clinical outcomes. However, the precision and dexterity required during these procedures poses considerable challenges for interventionists. Robotic systems have emerged offering transformative solutions, addressing issues such as operator fatigue, radiation exposure, and the inherent limitations of human precision. The integration of Embodied Intelligence (EI) into these systems signifies a paradigm shift, enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, advanced computer vision, medical image analysis, and machine learning techniques, are at the forefront of this evolution. These methods augment procedural intelligence by facilitating real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further refine navigation strategies and replicate experts' techniques. This review systematically examines the integration of EI principles into robotic technologies, in relation to endovascular procedures. We discuss recent advancements in intelligent perception and data-driven control, and their practical applications in robot-assisted endovascular procedures. By critically evaluating current limitations and emerging opportunities, this review establishes a framework for future developments, emphasizing the potential for greater autonomy and improved clinical outcomes. Emerging trends and specific areas of research, such as federated learning for medical data sharing, explainable AI for clinical decision support, and advanced human-robot collaboration paradigms, are also explored, offering insights into the future direction of this rapidly evolving field.
Understanding and analyzing video actions are essential for producing insightful and contextualized descriptions, especially for video-based applications like intelligent monitoring and autonomous systems. The proposed work introduces a novel framework for generating natural language descriptions from video datasets by combining textual and visual modalities. The suggested architecture makes use of ResNet50 to extract visual features from video frames that are taken from the Microsoft Research Video Description Corpus (MSVD), and Berkeley DeepDrive eXplanation (BDD-X) datasets. The extracted visual characteristics are converted into patch embeddings and then run through an encoder-decoder model based on Generative Pre-trained Transformer-2 (GPT-2). In order to align textual and visual representations and guarantee high-quality description production, the system uses multi-head self-attention and cross-attention techniques. The model's efficacy is demonstrated by performance evaluation using BLEU (1-4), CIDEr, METEOR, and ROUGE-L. The suggested framework outperforms traditional methods with BLEU-4 scores of 0.755 (BDD-X) and 0.778 (MSVD), CIDEr scores of 1.235 (BDD-X) and 1.315 (MSVD), METEOR scores of 0.312 (BDD-X) and 0.329 (MSVD), and ROUGE-L scores of 0.782 (BDD-X) and 0.795 (MSVD). By producing human-like, contextually relevant descriptions, strengthening interpretability, and improving real-world applications, this research advances explainable AI.
Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals' behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces 'hypothetical inference', a novel approach that uses language models to simulate how algorithms might interpret users' digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen adult participants, we identified three key design opportunities to foster critical algorithmic literacy: (1) reassembling scattered digital footprints into a unified map, (2) simulating algorithmic inference through LLM-generated interpretations, and (3) incorporating temporal dimensions to visualize evolving patterns. This research lays the groundwork for tools that can help users recognize the influence of data on platforms and develop greater autonomy in increasingly algorithm-mediated digital environments.
Deploying AI systems in public institutions can have far-reaching consequences for many people, making it a matter of public interest. Providing opportunities for stakeholders to come together, understand these systems, and debate their merits and harms is thus essential. Explainable AI often focuses on individuals, but deliberation benefits from group settings, which are underexplored. To address this gap, we present findings from an interview study with 8 focus groups and 12 individuals. Our findings provide insight into how explanations support AI novices in deliberating alone and in groups. Participants used modular explanations with four information categories to solve tasks and decide about an AI system's deployment. We found that the explanations supported groups in creating shared understanding and in finding arguments for and against the system's deployment. In comparison, individual participants engaged with explanations in more depth and performed better in the study tasks, but missed an exchange with others. Based on our findings, we provide suggestions on how explanations should be designed to work in group settings and describe their potential use in real-world contexts. With this, our contributions inform XAI research that aims to enable AI novices to understand and deliberate AI systems in the public sector.
Existing depression screening predominantly relies on standardized questionnaires (e.g., PHQ-9, BDI), which suffer from high misdiagnosis rates (18-34% in clinical studies) due to their static, symptom-counting nature and susceptibility to patient recall bias. This paper presents an AI-powered depression prevention system that leverages large language models (LLMs) to analyze real-time conversational cues--including subtle emotional expressions (e.g., micro-sentiment shifts, self-referential language patterns)--for more accurate and dynamic mental state assessment. Our system achieves three key innovations: (1) Continuous monitoring through natural dialogue, detecting depression-indicative linguistic features (anhedonia markers, hopelessness semantics) with 89% precision (vs. 72% for PHQ-9); (2) Adaptive risk stratification that updates severity levels based on conversational context, reducing false positives by 41% compared to scale-based thresholds; and (3) Personalized intervention strategies tailored to users' emotional granularity, demonstrating 2.3x higher adherence rates than generic advice. Clinical validation with 450 participants shows the system identifies 92% of at-risk cases missed by traditional scales, while its explainable AI interface bridges the gap between automated analysis and clinician judgment. This work establishes conversational AI as a paradigm shift from episodic scale-dependent diagnosis to continuous, emotionally intelligent mental health monitoring.
In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users' interoception challenging. This study aims to determine other forms of data that can explain users' interoceptive or similar states in their real-world lives and propose a novel hypothetical concept "cyberoception," a new sense (1) which has properties similar to interoception in terms of the correlation with other emotion-related abilities, and (2) which can be measured only by the sensors embedded inside commodity smartphone devices in users' daily lives. Results from a 10-day-long in-lab/in-the-wild hybrid experiment reveal a specific cyberoception type "Turn On" (users' subjective sensory perception about the frequency of turning-on behavior on their smartphones), significantly related to participants' emotional valence. We anticipate that cyberoception to serve as a fundamental building block for developing more "emotion-aware", user-friendly applications and services.
Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as a surrogate indicator for the quality of the entire sequence. However, this leads to sparse feedback and suboptimal token-level credit assignment. In this work, we frame reward shaping as an optimization problem focused on token-level credit assignment. We propose a reward-shaping function leveraging explainability methods such as SHAP and LIME to estimate per-token rewards from the reward model. To learn parameters of this shaping function, we employ a bilevel optimization framework that integrates Bayesian Optimization and policy training to handle noise from the token reward estimates. Our experiments show that achieving a better balance of token-level reward attribution leads to performance improvements over baselines on downstream tasks and finds an optimal policy faster during training. Furthermore, we show theoretically that explainability methods that are feature additive attribution functions maintain the optimal policy as the original reward.
Autonomous Sensory Meridian Response (ASMR) has been remarkably popular in the recent decade. While its effect has been validated through behavioral studies and neuro-physiological measurements such as electroencephalography (EEG) and related bio-signal analyses, its development and triggers remain a subject of debate. Previous studies suggest that its triggers are highly linked with cyclic patterns: predictable patterns introduce relaxation while variations maintain intrigue. To validate this and further understand the impact of acoustic features on ASMR effects, we designed three distinct cyclic patterns with monophonic and stereophonic variations, while controlling their predictability and randomness, and collected ASMR triggering scores through online surveys. Then, we extracted cyclic features and carried out regression analysis, seeking an explainable mapping of cyclic features and ASMR triggers. We found that relaxing effects accumulate progressively and are independent of spatial orientation. Cyclic patterns significantly influence psychological and physical effects, which remain invariant with time. Regression analysis revealed that smoothly spread and energy-dense cyclic patterns most effectively trigger ASMR responses.
Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a small student model from a larger teacher model. Previous research has introduced several distillation methods for both generating training data and for training the student model. Despite their relevance, the effects of state-of-the-art distillation methods on model performance and explainability have not been thoroughly investigated and compared. In this work, we enlarge the set of available methods by applying critique-revision prompting to distillation for data generation and by synthesizing existing methods for training. For these methods, we provide a systematic comparison based on the widely used Commonsense Question-Answering (CQA) dataset. While we measure performance via student model accuracy, we employ a human-grounded study to evaluate explainability. We contribute new distillation methods and their comparison in terms of both performance and explainability. This should further advance the distillation of small language models and, thus, contribute to broader applicability and faster diffusion of LLM technology.
Reinforcement Learning from Human Feedback (RLHF) has become the predominant approach for language model (LM) alignment. At its core, RLHF uses a margin-based loss for preference optimization, specifying ideal LM behavior only by the difference between preferred and dispreferred responses. In this paper, we identify a common pitfall of margin-based methods -- the under-specification of ideal LM behavior on preferred and dispreferred responses individually, which leads to two unintended consequences as the margin increases: (1) The probability of dispreferred (e.g., unsafe) responses may increase, resulting in potential safety alignment failures. (2) The probability of preferred responses may decrease, even when those responses are ideal. We demystify the reasons behind these problematic behaviors: margin-based losses couple the change in the preferred probability to the gradient of the dispreferred one, and vice versa, often preventing the preferred probability from increasing while the dispreferred one decreases, and thus causing a synchronized increase or decrease in both probabilities. We term this effect, inherent in margin-based objectives, gradient entanglement. Formally, we derive conditions for general margin-based alignment objectives under which gradient entanglement becomes concerning: the inner product of the gradients of preferred and dispreferred log-probabilities is large relative to the individual gradient norms. We theoretically investigate why such inner products can be large when aligning language models and empirically validate our findings. Empirical implications of our framework extend to explaining important differences in the training dynamics of various preference optimization algorithms, and suggesting potential algorithm designs to mitigate the under-specification issue of margin-based methods and thereby improving language model alignment.
As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8 evaluation metrics from 206 respondents. Subsequently, we fine-tuned different Large Language Models (LLMs) to predict average or individual human judgment across these metrics. Our methodology allowed LLMs to achieve an accuracy of up to 63% in zero-shot evaluations and 85% (over a 3-classes prediction) with fine-tuning across all metrics. The fine-tuned models predicting human ratings offer better comparability and scalability in evaluating different counterfactual explanation frameworks.
Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved -- acting as the elephant in the room within AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis identifies and examines nine persistent challenges that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: (1) the lack of clarity around what AI for education truly means -- often ignoring the distinct purposes, strengths, and limitations of different AI families -- and the trend of equating it with domain-agnostic, company-driven large language models; (2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature; (3) limited integration of domain knowledge and lack of stakeholder involvement in AI design and development; (4) continued use of non-sequential machine learning models on temporal educational data; (5) misuse of non-sequential metrics to evaluate sequential models; (6) use of unreliable explainable AI methods to provide explanations for black-box models; (7) ignoring ethical guidelines in addressing data inconsistencies during model training; (8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking; and (9) overemphasis on global prescriptions while overlooking localised, student-specific recommendations. Supported by theoretical and empirical research, we demonstrate how hybrid AI methods -- specifically neural-symbolic AI -- can address the elephant in the room and serve as the foundation for responsible, trustworthy AI systems in education.
The opaqueness of modern digital advertising, exemplified by platforms such as Meta Ads, raises concerns regarding their autonomous control over audience targeting, pricing structures, and ad relevancy assessments. Locked in their leading positions by network effects, ``Metas and Googles of the world'' attract countless advertisers who rely on intuition, with billions of dollars lost on ineffective social media ads. The platforms' algorithms use huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque as well. This lack of transparency hinders the advertisers' ability to make informed decisions and necessitates efforts to promote transparency, standardize industry metrics, and strengthen regulatory frameworks. In this work, we propose novel ways to assist marketers in optimizing their advertising strategies via machine learning techniques designed to analyze and evaluate content, in particular, predict the click-through rates (CTR) of novel advertising content. Another important problem is that large volumes of data available in the competitive landscape, e.g., competitors' ads, impede the ability of marketers to derive meaningful insights. This leads to a pressing need for a novel approach that would allow us to summarize and comprehend complex data. Inspired by the success of ChatGPT in bridging the gap between large language models (LLMs) and a broader non-technical audience, we propose a novel system that facilitates marketers in data interpretation, called SODA, that merges LLMs with explainable AI, enabling better human-AI collaboration with an emphasis on the domain of digital marketing and advertising. By combining LLMs and explainability features, in particular modern text-image models, we aim to improve the synergy between human marketers and AI systems.
The term islands in linguistics refers to phrases from which extracting an element results in ungrammaticality (Ross, 1967). Grammatical subjects are considered islands because extracting a sub-part of a subject results in an ill-formed sentence, despite having a clear intended meaning (e.g., "Which topic did the article about inspire you?"). The generative tradition, which views syntax as autonomous of meaning and function, attributes this ungrammaticality to the abstract movement dependency between the wh-phrase and the subject-internal position with which it is associated for interpretation. However, research on language that emphasizes its communicative function suggests instead that syntactic constraints, including islands, can be explained based on the way different constructions package information. Accordingly, Abeill\'e et al. (2020) suggest that the islandhood of subjects is specific to the information structure of wh-questions, and propose that subjects are not islands for movement, but for focusing, due to their discourse-backgroundedness. This predicts that other constructions that differ in their information structure from wh-questions, but still involve movement, should not create a subject island effect. We test this prediction in three large-scale acceptability studies, using a super-additive design that singles out subject island violations, in three different constructions: wh-questions, relative clauses, and topicalization. We report evidence for a subject island effect in each construction type, despite only wh-questions introducing what Abeill\'e et al. (2020) call "a clash in information structure." We argue that this motivates an account of islands in terms of abstract, syntactic representations, independent of the communicative function associated with the constructions.
Increasingly, people use social media for their day-to-day interactions and as a source of information, even though much of this information is practically anonymous. This raises the question: does anonymous information influence its recipients? We conducted an online, two-phase, preregistered experiment using a nationally representative sample of participants from the U.S. to find the answer. To avoid biases of opinions among participants, in the first phase, each participant examines ten Rorschach inkblots and chooses one of four opinions assigned to each inkblot. In the second phase, the participants are randomly assigned to one of four distinct information conditions and are asked to revisit their opinions for the same ten inkblots. Conditions ranged from repeating phase one to receiving anonymous comments about certain opinions. Results were consistent with the preregistration. Importantly, anonymous comments shown in phase two influence up to half of the participants' opinion selections. To better understand the role of anonymous comments in influencing the selections of opinions, we implemented agent-based modeling (ABM). ABM results suggest that a straightforward mechanism can explain the impact of such information. Overall, our results indicate that even anonymous information can have a significant impact on its recipients, potentially altering their popularity rankings. However, the strength of such influence weakens when recipients' confidence in their selections increases. Additionally, we found that participants' confidence in the first phase is inversely related to the number of change opinions.