mstyslavity/prisma_v3 huggingface dataset:
The opacity of AI models necessitates both validation and evaluation before their integration into services. To investigate these models, explainable AI (XAI) employs methods that elucidate the relationship between input features and output predictions. The operations of XAI extend beyond the execution of a single algorithm, involving a series of activities that include preprocessing data, adjusting XAI to align with model parameters, invoking the model to generate predictions, and summarizing the XAI results. Adversarial attacks are well-known threats that aim to mislead AI models. The assessment complexity, especially for XAI, increases when open-source AI models are subject to adversarial attacks, due to various combinations. To automate the numerous entities and tasks involved in XAI-based assessments, we propose a cloud-based service framework that encapsulates computing components as microservices and organizes assessment tasks into pipelines. The current XAI tools are not inherently service-oriented. This framework also integrates open XAI tool libraries as part of the pipeline composition. We demonstrate the application of XAI services for assessing five quality attributes of AI models: (1) computational cost, (2) performance, (3) robustness, (4) explanation deviation, and (5) explanation resilience across computer vision and tabular cases. The service framework generates aggregated analysis that showcases the quality attributes for more than a hundred combination scenarios.
Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative features of neurological disorders through spontaneous speech. However, LLMs typically lack interpretability, meaning they do not provide clear and specific reasons for their decisions. Therefore, there is a need for methods capable of identifying the representative features of neurological disorders in speech and explaining clearly why these features are relevant. This paper presents an explainable LLM method, named SLIME (Statistical and Linguistic Insights for Model Explanation), capable of identifying lexical components representative of AD and indicating which components are most important for the LLM's decision. In developing this method, we used an English-language dataset consisting of transcriptions from the Cookie Theft picture description task. The LLM Bidirectional Encoder Representations from Transformers (BERT) classified the textual descriptions as either AD or control groups. To identify representative lexical features and determine which are most relevant to the model's decision, we used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and Word Count (LIWC), and statistical analysis. Our method demonstrates that BERT leverages lexical components that reflect a reduction in social references in AD and identifies which further improve the LLM's accuracy. Thus, we provide an explainability tool that enhances confidence in applying LLMs to neurological clinical contexts, particularly in the study of neurodegeneration.
Black-hole (BH) high-mass X-ray binary (HMXB) systems are likely to be the progenitors of BH-BH mergers detected by LIGO/Virgo/KAGRA (LVK). Yet merging BHs reach higher masses ($\sim 100M_{\odot}$) than BHs in HMXBs ($\sim 20 M_{\odot}$) and exhibit lower spins ($a_{\rm BH}\lesssim 0.25$ with a larger values tail) than what is often claimed for BHs in HMXBs ($a_{\rm BH}\gtrsim 0.9$). This could suggest that these two classes of systems belong to different populations, but here we show that this may not necessarily be the case. The difference in masses is easily explained as the known HMXB-BHs are in galaxies with relatively high metallicity, so their progenitor stars are subject to strong mass loss from winds, leading to relatively low-mass BH at core collapse. Conversely, LVK is also able to detect BHs from low-metallicity galaxies that produce more massive stellar-origin BHs. The difference in spin is more difficult to explain. Models with efficient angular momentum transport in stellar interiors produce slowly spinning progenitors for both LVK and HMXB BHs. Known HMXBs have orbital periods that are too long for tidal spin-up and are unlikely to have undergone significant accretion spin-up. Instead, we show that the derived value of the BH spin depends strongly on how the HMXB accretion disc emission is modelled. We argue that since Cyg X-1 is never observed in a soft state, the appropriate spectral models must take into account the Comptonisation of the disc photosphere. We show that such models are consistent with low spin values, namely: $a_{\rm BH}\sim 0.1$. This was confirmed by other teams for both Cyg X-1 and LMC X-1 and we show this is also the case for M33 X-7. We conclude that all HMXB BHs can exhibit low spins, in accordance with stellar evolution models. Hence, the observations are consistent with the LVK BHs and HMXB BHs belonging to the same population.
Despite the discovery of multiple intrinsic magnetic topological insulators in recent years the observation of Chern insulators is still restricted to very low temperatures due to the negligible charge gaps. Here, we uncover the potential of heavy transition-metal compounds for realizing a collinear antiferromagnetic Chern insulator (AFCI) with a charge gap as large as 300 meV. Our analysis relies on the Kane-Mele-Kondo model with a ferromagnetic Hund coupling $J_{\rm H}$ between the spins of itinerant electrons and the localized spins of size $S$. We show that a spin-orbit coupling $\lambda_{\rm SO} \gtrsim 0.03t$, where $t$ is the nearest-neighbor hopping element, is already large enough to stabilize an AFCI provided the alternating sublattice potential $\delta$ is in the range $\delta \approx SJ_{\rm H}$. We establish a remarkable increase in the charge gap upon increasing $\lambda_{\rm SO}$ in the AFCI phase. Using our results we explain the collinear AFCI recently found in monolayers of CrO and MoO with charge gaps of 1 and $50$ meV, respectively. In addition, we propose bilayers of heavy transition-metal oxides of perovskite structure as candidates to realize a room-temperature AFCI if grown along the $[111]$ direction and subjected to a perpendicular electric field.
Natural language and visualization are two complementary modalities of human communication that play a crucial role in conveying information effectively. While visualizations help people discover trends, patterns, and anomalies in data, natural language descriptions help explain these insights. Thus, combining text with visualizations is a prevalent technique for effectively delivering the core message of the data. Given the rise of natural language generation (NLG), there is a growing interest in automatically creating natural language descriptions for visualizations, which can be used as chart captions, answering questions about charts, or telling data-driven stories. In this survey, we systematically review the state of the art on NLG for visualizations and introduce a taxonomy of the problem. The NLG tasks fall within the domain of Natural Language Interfaces (NLI) for visualization, an area that has garnered significant attention from both the research community and industry. To narrow down the scope of the survey, we primarily concentrate on the research works that focus on text generation for visualizations. To characterize the NLG problem and the design space of proposed solutions, we pose five Wh-questions, why and how NLG tasks are performed for visualizations, what the task inputs and outputs are, as well as where and when the generated texts are integrated with visualizations. We categorize the solutions used in the surveyed papers based on these "five Wh-questions." Finally, we discuss the key challenges and potential avenues for future research in this domain.
Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantly enhanced the processing of such records, streamlining workflows and reducing manual data entry, thereby saving healthcare providers significant resources. However, the black-box nature of these models often leaves healthcare professionals hesitant to trust them. State-of-the-art explainability methods increase model transparency but rely on human-annotated evidence spans, which are costly. In this study, we propose an approach to produce plausible and faithful explanations without needing such annotations. We demonstrate on the automated medical coding task that adversarial robustness training improves explanation plausibility and introduce AttInGrad, a new explanation method superior to previous ones. By combining both contributions in a fully unsupervised setup, we produce explanations of comparable quality, or better, to that of a supervised approach. We release our code and model weights.
Electroadhesion (EA) has potential in robotics, automation, space missions, textiles, and tactile displays, but its physics remains underexplored due to limited models and experimental data. This thesis develops an electro-mechanical model to estimate electrostatic forces between human finger and touchscreen under EA and compares it to experimentally measured friction forces. The model aligns well with the data, showing that the electrostatic force changes mainly due to charge leakage from the Stratum Corneum at frequencies below 250 Hz and its electrical properties above 250 Hz. Additionally, a novel approach using electrical impedance measurements estimates electrostatic forces by subtracting skin and touchscreen impedances from the total impedance. This method is the first to experimentally estimate the average air gap between finger and voltage-induced capacitive touchscreen. The effect of electrode polarization impedance, particularly at low frequencies, was also studied, revealing its role in the charge leakage phenomenon. Tactile perception via EA was investigated using DC and AC voltage signals on a touchscreen with 10 participants of varying finger moisture levels. Results showed that AC voltage detection thresholds were significantly lower than for DC, explained by charge leakage at lower frequencies. Participants with moist fingers exhibited higher threshold levels, supported by impedance measurements. The thesis also investigated how touchscreen top coatings influence tactile perception, focusing on EA-free interactions. Psychophysical experiments and physical measurements demonstrated that coating materials significantly affect tactile perception, likely due to molecular interactions. These findings offer insights into finger-touchscreen interactions under EA and have potential applications in designing robotic systems and haptic interfaces using this technology.
Memory is the foundation of all human activities; without memory, it would be nearly impossible for people to perform any task in daily life. With the development of Large Language Models (LLMs), their language capabilities are becoming increasingly comparable to those of humans. But do LLMs have memory? Based on current performance, LLMs do appear to exhibit memory. So, what is the underlying mechanism of this memory? Previous research has lacked a deep exploration of LLMs' memory capabilities and the underlying theory. In this paper, we use Universal Approximation Theorem (UAT) to explain the memory mechanism in LLMs. We also conduct experiments to verify the memory capabilities of various LLMs, proposing a new method to assess their abilities based on these memory ability. We argue that LLM memory operates like Schr\"odinger's memory, meaning that it only becomes observable when a specific memory is queried. We can only determine if the model retains a memory based on its output in response to the query; otherwise, it remains indeterminate. Finally, we expand on this concept by comparing the memory capabilities of the human brain and LLMs, highlighting the similarities and differences in their operational mechanisms.
Predicting and explaining the private information contained in an image in human-understandable terms is a complex and contextual task. This task is challenging even for large language models. To facilitate the understanding of privacy decisions, we propose to predict image privacy based on a set of natural language content descriptors. These content descriptors are associated with privacy scores that reflect how people perceive image content. We generate descriptors with our novel Image-guided Topic Modeling (ITM) approach. ITM leverages, via multimodality alignment, both vision information and image textual descriptions from a vision language model. We use the ITM-generated descriptors to learn a privacy predictor, Priv$\times$ITM, whose decisions are interpretable by design. Our Priv$\times$ITM classifier outperforms the reference interpretable method by 5 percentage points in accuracy and performs comparably to the current non-interpretable state-of-the-art model.
The sudden emergence of large language models (LLMs) such as ChatGPT has had a disruptive impact throughout the computing education community. LLMs have been shown to excel at producing correct code to CS1 and CS2 problems, and can even act as friendly assistants to students learning how to code. Recent work shows that LLMs demonstrate unequivocally superior results in being able to explain and resolve compiler error messages -- for decades, one of the most frustrating parts of learning how to code. However, LLM-generated error message explanations have only been assessed by expert programmers in artificial conditions. This work sought to understand how novice programmers resolve programming error messages (PEMs) in a more realistic scenario. We ran a within-subjects study with $n$ = 106 participants in which students were tasked to fix six buggy C programs. For each program, participants were randomly assigned to fix the problem using either a stock compiler error message, an expert-handwritten error message, or an error message explanation generated by GPT-4. Despite promising evidence on synthetic benchmarks, we found that GPT-4 generated error messages outperformed conventional compiler error messages in only 1 of the 6 tasks, measured by students' time-to-fix each problem. Handwritten explanations still outperform LLM and conventional error messages, both on objective and subjective measures.
Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different device placements). This paper proposes a Deep Heterogeneous Contrastive Hyper-Graph Learning (DHC-HGL) framework that captures heterogenous Context-Aware HAR (CA-HAR) hypergraph properties in a message-passing and neighborhood-aggregation fashion. Prior work only explored homogeneous or shallow-node-heterogeneous graphs. DHC-HGL handles heterogeneous CA-HAR data by innovatively 1) Constructing three different types of sub-hypergraphs that are each passed through different custom HyperGraph Convolution (HGC) layers designed to handle edge-heterogeneity and 2) Adopting a contrastive loss function to ensure node-heterogeneity. In rigorous evaluation on two CA-HAR datasets, DHC-HGL significantly outperformed state-of-the-art baselines by 5.8% to 16.7% on Matthews Correlation Coefficient (MCC) and 3.0% to 8.4% on Macro F1 scores. UMAP visualizations of learned CA-HAR node embeddings are also presented to enhance model explainability.
Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.
This paper presents a system called Robo-CSK-Organizer that infuses commonsense knowledge from a classical knowledge based to enhance the context recognition capabilities of robots so as to facilitate the organization of detected objects by classifying them in a task-relevant manner. It is particularly useful in multipurpose robotics. Unlike systems relying solely on deep learning tools such as ChatGPT, the Robo-CSK-Organizer system stands out in multiple avenues as follows. It resolves ambiguities well, and maintains consistency in object placement. Moreover, it adapts to diverse task-based classifications. Furthermore, it contributes to explainable AI, hence helping to improve trust and human-robot collaboration. Controlled experiments performed in our work, simulating domestic robotics settings, make Robo-CSK-Organizer demonstrate superior performance while placing objects in contextually relevant locations. This work highlights the capacity of an AI-based system to conduct commonsense-guided decision-making in robotics closer to the thresholds of human cognition. Hence, Robo-CSK-Organizer makes positive impacts on AI and robotics.
The application of Shapley values to high-dimensional, time-series-like data is computationally challenging - and sometimes impossible. For $N$ inputs the problem is $2^N$ hard. In image processing, clusters of pixels, referred to as superpixels, are used to streamline computations. This research presents an efficient solution for time-seres-like data that adapts the idea of superpixels for Shapley value computation. Motivated by a forensic DNA classification example, the method is applied to multivariate time-series-like data whose features have been classified by a convolutional neural network (CNN). In DNA processing, it is important to identify alleles from the background noise created by DNA extraction and processing. A single DNA profile has $31,200$ scan points to classify, and the classification decisions must be defensible in a court of law. This means that classification is routinely performed by human readers - a monumental and time consuming process. The application of a CNN with fast computation of meaningful Shapley values provides a potential alternative to the classification. This research demonstrates the realistic, accurate and fast computation of Shapley values for this massive task
There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.
Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, more importantly, what information it lacks. To answer this question we conducted an online study with 200 participants, which allowed us to assess explainees' ability to realise explicated information -- i.e., factual insights conveyed by an explanation -- and unspecified information -- i.e, insights that are not communicated by an explanation -- across four representative explanation types: model architecture, decision surface visualisation, counterfactual explainability and feature importance. Our findings uncover that highly comprehensible explanations, e.g., feature importance and decision surface visualisation, are exceptionally susceptible to misinterpretation since users tend to infer spurious information that is outside of the scope of these explanations. Additionally, while the users gauge their confidence accurately with respect to the information explicated by these explanations, they tend to be overconfident when misinterpreting the explanations. Our work demonstrates that human comprehension can be a double-edged sword since highly accessible explanations may convince users of their truthfulness while possibly leading to various misinterpretations at the same time. Machine learning explanations should therefore carefully navigate the complex relation between their full scope and limitations to maximise understanding and curb misinterpretation.
Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm manufactures are aimed to their robots could increasingly perform tasks collaborating with humans. One of the ways to improve this collaboration is by making the movement of robots more humanlike. This way, it would be easier for a human to foresee the movement of the robot and approach it without fear of contact. The main difference between the movement of a human and of a robotic arm is that the former has a bell-shaped speed profile while the latter has a uniform speed one. To generate this speed profile, the kinematic theory of rapid human movements and its Sigma-Lognormal model has been used. This model is widely used to explain most of the basic phenomena related to the control of human movements. Both human-like and robotic-like movements are transferred to the UR3 robot. In this paper we detail the how the UR3 robot was programmed to produce both kinds of movement. The dissimilarities result between the input motion and output motion to the robot confirm the possibility to develop human-like velocities in the UR3 robot.