mstyslavity/prisma_v3 huggingface dataset:
README and CONTRIBUTING files can serve as the first point of contact for potential contributors to free/libre and open source software (FLOSS) projects. Prominent open source software organizations such as Mozilla, GitHub, and the Linux Foundation advocate that projects provide community-focused and process-oriented documentation early to foster recruitment and activity. In this paper we investigate the introduction of these documents in FLOSS projects, including whether early documentation conforms to these recommendations or explains subsequent activity. We use a novel dataset of FLOSS projects packaged by the Debian GNU/Linux distribution and conduct a quantitative analysis to examine README (n=4226) and CONTRIBUTING (n=714) files when they are first published into projects' repositories. We find that projects create minimal READMEs proactively, but often publish CONTRIBUTING files following an influx of contributions. The initial versions of these files rarely focus on community development, instead containing descriptions of project procedure for library usage or code contribution. The findings suggest that FLOSS projects do not create documentation with community-building in mind, but rather favor brevity and standardized instructions.
Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize healthcare by enabling continual health monitoring, disease prediction, and routine recognition. Despite the high accuracy of Deep Learning (DL) HAR models, their robustness to real-world variabilities remains untested, as they have primarily been trained and tested on limited lab-confined data. In this study, we isolate subject, device, position, and orientation variability to determine their effect on DL HAR models and assess the robustness of these models in real-world conditions. We evaluated the DL HAR models using the HARVAR and REALDISP datasets, providing a comprehensive discussion on the impact of variability on data distribution shifts and changes in model performance. Our experiments measured shifts in data distribution using Maximum Mean Discrepancy (MMD) and observed DL model performance drops due to variability. We concur that studied variabilities affect DL HAR models differently, and there is an inverse relationship between data distribution shifts and model performance. The compounding effect of variability was analyzed, and the implications of variabilities in real-world scenarios were highlighted. MMD proved an effective metric for calculating data distribution shifts and explained the drop in performance due to variabilities in HARVAR and REALDISP datasets. Combining our understanding of variability with evaluating its effects will facilitate the development of more robust DL HAR models and optimal training techniques. Allowing Future models to not only be assessed based on their maximum F1 score but also on their ability to generalize effectively
Precise elevation perception in binaural audio remains a challenge, despite extensive research on head-related transfer functions (HRTFs) and spectral cues. While prior studies have advanced our understanding of sound localization cues, the interplay between spectral features and elevation perception is still not fully understood. This paper presents a comprehensive analysis of over 600 subjects from 11 diverse public HRTF datasets, employing a convolutional neural network (CNN) model combined with explainable artificial intelligence (XAI) techniques to investigate elevation cues. In addition to testing various HRTF pre-processing methods, we focus on both within-dataset and inter-dataset generalization and explainability, assessing the model's robustness across different HRTF variations stemming from subjects and measurement setups. By leveraging class activation mapping (CAM) saliency maps, we identify key frequency bands that may contribute to elevation perception, providing deeper insights into the spectral features that drive elevation-specific classification. This study offers new perspectives on HRTF modeling and elevation perception by analyzing diverse datasets and pre-processing techniques, expanding our understanding of these cues across a wide range of conditions.
Alzheimer's disease, a neurodegenerative disorder, is associated with neural, genetic, and proteomic factors while affecting multiple cognitive and behavioral faculties. Traditional AD prediction largely focuses on univariate disease outcomes, such as disease stages and severity. Multimodal data encode broader disease information than a single modality and may, therefore, improve disease prediction; but they often contain missing values. Recent "deeper" machine learning approaches show promise in improving prediction accuracy, yet the biological relevance of these models needs to be further charted. Integrating missing data analysis, predictive modeling, multimodal data analysis, and explainable AI, we propose OPTIMUS, a predictive, modular, and explainable machine learning framework, to unveil the many-to-many predictive pathways between multimodal input data and multivariate disease outcomes amidst missing values. OPTIMUS first applies modality-specific imputation to uncover data from each modality while optimizing overall prediction accuracy. It then maps multimodal biomarkers to multivariate outcomes using machine-learning and extracts biomarkers respectively predictive of each outcome. Finally, OPTIMUS incorporates XAI to explain the identified multimodal biomarkers. Using data from 346 cognitively normal subjects, 608 persons with mild cognitive impairment, and 251 AD patients, OPTIMUS identifies neural and transcriptomic signatures that jointly but differentially predict multivariate outcomes related to executive function, language, memory, and visuospatial function. Our work demonstrates the potential of building a predictive and biologically explainable machine-learning framework to uncover multimodal biomarkers that capture disease profiles across varying cognitive landscapes. The results improve our understanding of the complex many-to-many pathways in AD.
The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-understandable concepts. However, this inherent interpretability comes at the cost of greater annotation burden. Additionally, adding new concepts requires retraining the entire system. In this work, we introduce a novel two-step methodology that addresses both of these challenges. By simulating the two stages of a CBM, we utilize a pretrained Vision Language Model (VLM) to automatically predict clinical concepts, and an off-the-shelf Large Language Model (LLM) to generate disease diagnoses based on the predicted concepts. Furthermore, our approach supports test-time human intervention, enabling corrections to predicted concepts, which improves final diagnoses and enhances transparency in decision-making. We validate our approach on three skin lesion datasets, demonstrating that it outperforms traditional CBMs and state-of-the-art explainable methods, all without requiring any training and utilizing only a few annotated examples. The code is available at https://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis.
The human capacity for working together and with tools builds on cognitive abilities that, while not unique to humans, are most developed in humans both in scale and plasticity. Our capacity to engage with collaborators and with technology requires a continuous expenditure of attentive work that we show may be understood in terms of what is heuristically argued as`trust' in socio-economic fields. By adopting a `social physics' of information approach, we are able to bring dimensional analysis to bear on an anthropological-economic issue. The cognitive-economic trade-off between group size and rate of attention to detail is the connection between these. This allows humans to scale cooperative effort across groups, from teams to communities, with a trade-off between group size and attention. We show here that an accurate concept of trust follows a bipartite `economy of work' model, and that this leads to correct predictions about the statistical distribution of group sizes in society. Trust is essentially a cognitive-economic issue that depends on the memory cost of past behaviour and on the frequency of attentive policing of intent. All this leads to the characteristic `fractal' structure for human communities. The balance between attraction to some alpha attractor and dispersion due to conflict fully explains data from all relevant sources. The implications of our method suggest a broad applicability beyond purely social groupings to general resource constrained interactions, e.g. in work, technology, cybernetics, and generalized socio-economic systems of all kinds.
Bias from contrast injection variability is a significant obstacle to accurate intracranial aneurysm occlusion prediction using quantitative angiography and deep neural networks . This study explores bias removal and explainable AI for outcome prediction. This study used angiograms from 458 patients with flow diverters treated IAs with six month follow up defining occlusion status. We minimized injection variability by deconvolving the parent artery input to isolate the impulse response of aneurysms, then reconvolving it with a standardized injection curve. A deep neural network trained on these QA derived biomarkers predicted six month occlusion. Local Interpretable Model Agnostic Explanations identified the key imaging features influencing the model, ensuring transparency and clinical relevance.
This late-breaking work presents a large-scale analysis of explainable AI (XAI) literature to evaluate claims of human explainability. We collaborated with a professional librarian to identify 18,254 papers containing keywords related to explainability and interpretability. Of these, we find that only 253 papers included terms suggesting human involvement in evaluating an XAI technique, and just 128 of those conducted some form of a human study. In other words, fewer than 1% of XAI papers (0.7%) provide empirical evidence of human explainability when compared to the broader body of XAI literature. Our findings underscore a critical gap between claims of human explainability and evidence-based validation, raising concerns about the rigor of XAI research. We call for increased emphasis on human evaluations in XAI studies and provide our literature search methodology to enable both reproducibility and further investigation into this widespread issue.
In this study, we examined whether a short-form AI literacy intervention could reduce the adoption of incorrect recommendations from large language models. High school seniors were randomly assigned to either a control or an intervention group, which received an educational text explaining ChatGPT's working mechanism, limitations, and proper use. Participants solved math puzzles with the help of ChatGPT's recommendations, which were incorrect in half of the cases. Results showed that students adopted incorrect suggestions 52.1% of the time, indicating widespread over-reliance. The educational intervention did not significantly reduce over-reliance. Instead, it led to an increase in ignoring ChatGPT's correct recommendations. We conclude that the usage of ChatGPT is associated with over-reliance and it is not trivial to increase AI literacy to counter over-reliance.
Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the part-prototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability. Code is available at: https://github.com/zijizhu/proto-non-param.
Active matter spans a wide range of time and length scales, from groups of cells and synthetic self-propelled particles to schools of fish, flocks of birds, or even human crowds. The theoretical framework describing these systems has shown tremendous success at finding universal phenomenology. However, further progress is often burdened by the difficulty of determining the forces that control the dynamics of the individual elements within each system. Accessing this local information is key to understanding the physics dominating the system and to create the models that can explain the observed collective phenomena. In this work, we present a machine-learning model, a graph neural network, that uses the collective movement of the system to learn the active and two-body forces controlling the individual dynamics of the particles. We verify our approach using numerical simulations of active brownian particles, considering different interaction potentials and levels of activity. Finally, we apply our model to experiments of electrophoretic Janus particles, extracting the active and two-body forces that control the dynamics of the colloids. Due to this, we can uncover the physics dominating the behavior of the system. We extract an active force that depends on the electric field and also area fraction. We also discover a dependence of the two-body interaction with the electric field that leads us to propose that the dominant force between these colloids is a screened electrostatic interaction with a constant length scale. We expect that this methodology can open a new avenue for the study and modeling of experimental systems of active particles.
Time-dependent Stark-Zeeman systems describe the motion of an electron attracted by a proton subject to a magnetic and a time-dependent electric field. For instance the study of the dynamics of a gateway around the moon which is subject to the joint attraction of the moon, the earth and the sun leads to time-dependent Stark-Zeeman systems. In the time-dependent case there is no preserved energy. Therefore collisions cannot be regularized by blowing up the energy hypersurface. A new regularization technique of blowing up instead of the energy hypersurface the loop space was recently discovered by Barutello, Ortega, and Verzini. In this article we explain how this new regularization technique can be applied to the study of periodic orbits in time-dependent planar Stark-Zeeman systems. Since the regularization by blowing-up the loop space is nonlocal the regularized periodic orbits will not satisfy an ODE anymore but a delay equation.
We introduce AutoPersuade, a three-part framework for constructing persuasive messages. First, we curate a large dataset of arguments with human evaluations. Next, we develop a novel topic model to identify argument features that influence persuasiveness. Finally, we use this model to predict the effectiveness of new arguments and assess the causal impact of different components to provide explanations. We validate AutoPersuade through an experimental study on arguments for veganism, demonstrating its effectiveness with human studies and out-of-sample predictions.
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely trigger analytical thinking and face difficulties in communicating the nuances of conflicting opinions to the AI when disagreements occur. To tackle this challenge, we propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates. To empower AI with deliberative capabilities, we designed Deliberative AI, which leverages large language models (LLMs) as a bridge between humans and domain-specific models to enable flexible conversational interactions and faithful information provision. An exploratory evaluation on a graduate admissions task shows that Deliberative AI outperforms conventional explainable AI (XAI) assistants in improving humans' appropriate reliance and task performance. Based on a mixed-methods analysis of participant behavior, perception, user experience, and open-ended feedback, we draw implications for future AI-assisted decision tool design.
Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their semantic quality is comparable to human-written ones. This phenomenon, known as source bias, threatens the sustainable development of the information access ecosystem. However, the underlying causes of source bias remain unexplored. In this paper, we explain the process of information retrieval with a causal graph and discover that PLM-based retrievers learn perplexity features for relevance estimation, causing source bias by ranking the documents with low perplexity higher. Theoretical analysis further reveals that the phenomenon stems from the positive correlation between the gradients of the loss functions in language modeling task and retrieval task. Based on the analysis, a causal-inspired inference-time debiasing method is proposed, called Causal Diagnosis and Correction (CDC). CDC first diagnoses the bias effect of the perplexity and then separates the bias effect from the overall estimated relevance score. Experimental results across three domains demonstrate the superior debiasing effectiveness of CDC, emphasizing the validity of our proposed explanatory framework. Source codes are available at https://github.com/WhyDwelledOnAi/Perplexity-Trap.
Modern telescopes provide breathtaking images of nebulae, clouds and galaxies shaped by gravity-driven interactions between complex bodies. While such structures are prevalent on an astrophysical scale, they are rarely observed at the human scale. In this letter, we report the observations of the complex orbits, collision, and coalescence of droplets on a soap film, forming structures such as bridges and spiral arms, reminiscent of their astrophysical counterparts. These dynamics emerge from attractive forces caused by gravito-capillary-driven distortions of the supporting soap film. Long orbits and intricate coalescence mechanisms are enabled by the small dissipation in the soap film and the fluidic nature of the droplets and supporting film, respectively. The existence of stable droplets within the soap film featuring a universal radius, as well as the attractive potentials, are explained through a careful comparison of experimental data with models computing the distortions of the supporting soap film. This work opens perspectives to
Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally whitebox in nature and not practical as they can be easily detected by humans E.g. Changing the word from "Poor" to "Rich". We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.
"What cannot be measured cannot be improved" while likely never uttered by Lord Kelvin, summarizes effectively the purpose of this work. This paper presents a detailed evaluation of automated metrics for evaluating structured 3D reconstructions. Pitfalls of each metric are discussed, and a thorough analyses through the lens of expert 3D modelers' preferences is presented. A set of systematic "unit tests" are proposed to empirically verify desirable properties, and context aware recommendations as to which metric to use depending on application are provided. Finally, a learned metric distilled from human expert judgments is proposed and analyzed.
Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagnosis, health monitoring, and biometric authentication. While valuable for healthcare, they also carry unique biometric identifiers that pose privacy risks, especially when ECG data shared across multiple entities. These risks are amplified in shared environments, where re-identification threats can compromise patient privacy. Existing deep learning re-identification models prioritize accuracy but lack explainability, making it challenging to understand how the unique biometric characteristics encoded within ECG signals are recognized and utilized for identification. Without these insights, despite high accuracy, developing secure and trustable ECG data-sharing frameworks remains difficult, especially in diverse, multi-source environments. In this work, we introduce TransECG, a Vision Transformer (ViT)-based method that uses attention mechanisms to pinpoint critical ECG segments associated with re-identification tasks like gender, age, and participant ID. Our approach demonstrates high accuracy (89.9% for gender, 89.9% for age, and 88.6% for ID re-identification) across four real-world datasets with 87 participants. Importantly, we provide key insights into ECG components such as the R-wave, QRS complex, and P-Q interval in re-identification. For example, in the gender classification, the R wave contributed 58.29% to the model's attention, while in the age classification, the P-R interval contributed 46.29%. By combining high predictive performance with enhanced explainability, TransECG provides a robust solution for privacy-conscious ECG data sharing, supporting the development of secure and trusted healthcare data environment.