mstyslavity/prisma_v3 huggingface dataset:
Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson's terms. Second, we construct a Legal Question Bank (LQB), which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender (CRec). Given a user's verbal description of a legal situation that requires a legal solution, CRec interprets the user's input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions (MGQs) against human-composed questions (HCQs) and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public.
Blockchain technology has set off a wave of decentralization in the world since its birth. The trust system constructed by blockchain technology based on cryptography algorithm and computing power provides a practical and powerful solution to solve the trust problem in human society. In order to make more convenient use of the characteristics of blockchain and build applications on it, smart contracts appear. By defining some trigger automatic execution contracts, the application space of blockchain is expanded and the foundation for the rapid development of blockchain is laid. This is blockchain 2.0. However, the programmability of smart contracts also introduces vulnerabilities. In order to cope with the insufficient security guarantee of high-value application networks running on blockchain 2.0 and smart contracts, this article will be represented by Ethereum to introduce the technical details of understanding blockchain 2.0 and the operation principle of contract virtual machines, and explain how cryptocurrencies based on blockchain 2.0 are constructed and operated. The common security problems and solutions are also discussed. Based on relevant research and on-chain practice, this paper provides a complete and comprehensive perspective to understanding cryptocurrency technology based on blockchain 2.0 and provides a reference for building more secure cryptocurrency contracts.
This study explores how emotions and sentiments differ in customer reviews of products and services on e-commerce platforms. Unlike earlier research that treats all reviews uniformly, this study distinguishes between reviews of products, typically fulfilling basic, functional needs, and services, which often cater to experiential and emotional desires. The findings reveal clear differences in emotional expression and sentiment between the two. Product reviews frequently focus on practicality, such as functionality, reliability, and value for money, and are generally more neutral or pragmatic in tone. In contrast, service reviews involve stronger emotional engagement, as services often entail personal interactions and subjective experiences. Customers express a broader spectrum of emotions, such as joy, frustration, or disappointment when reviewing services, as identified using advanced machine learning techniques. Cultural background further influences these patterns. Consumers from collectivist cultures, as defined by Hofstede cultural dimensions, often use more moderated and socially considerate language, reflecting an emphasis on group harmony. Conversely, consumers from individualist cultures tend to offer more direct, emotionally intense feedback. Notably, gender appears to have minimal impact on sentiment variation, reinforcing the idea that the nature of the offering (product vs. service) and cultural context are the dominant factors. Theoretically, the study extends Maslow hierarchy of needs and Hofstede cultural framework to the domain of online reviews, proposing a model that explains how these dimensions shape consumer expression. Practically, the insights offer valuable guidance for businesses looking to optimize their marketing and customer engagement strategies by aligning messaging and service design with customer expectations across product types and cultural backgrounds.
Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems.
A number of experiments have reported evidence for the existence of the lower Josephson plasmon mode in underdoped YBCO up to the pseudogap temperature scale when the sample is subject to intense terahertz pulses. Evidences include the observation of a reflectivity edge that resembles that of the superconducting state, and the second harmonic generation of a probe optical pulse that is modulated at a frequency similar the reflectivity feature. Since the lower Josephson plasmon mode is often associated with coherent oscillations between bilayers in the YBCO structure, these experiments have led to the suggestion that the intense pump has created pair coherence up to 350K. In this paper, we propose an alternative explanation of these experiments based on the model of short ranged superconducting correlations in the equilibrium state and using the Floquet perspective to analyze optical responses of the photoexcited state. Our model only requires local pairing with phase correlations that can be very short ranged when the system is at equilibrium at a temperature above Tc but below the pseudo-gap temperature T*. Within this assumption there is no phase coherence between bilayers. On the other hand the relative phase between members of the bilayer has a longer in-plane correlation which leads locally to a finite Josephson current. We show that the nonlinearity afforded by the local intra-bilayer Josephson current is sufficient to explain both the reflectivity and second harmonic generation data. The key point is that in the lower Josephson plasmon, the coupling between bilayers is mainly capacitive: the Josepson current between bilayers can be set to zero without affecting the parametric amplification process. The implication is that while superconducting coherence may not be created by the pump, the pseudogap phase must possess a local pairing amplitude at equilibrium.
Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.
Continuous monitoring of behavior and physiology via wearable devices offers a novel, objective method for the early detection of worsening depression and anxiety. In this study, we present an explainable anomaly detection framework that identifies clinically meaningful increases in symptom severity using consumer-grade wearable data. Leveraging data from 2,023 participants with defined healthy baselines, our LSTM autoencoder model learned normal health patterns of sleep duration, step count, and resting heart rate. Anomalies were flagged when self-reported depression or anxiety scores increased by >=5 points (a threshold considered clinically significant). The model achieved an adjusted F1-score of 0.80 (precision = 0.73, recall = 0.88) in detecting 393 symptom-worsening episodes across 341 participants, with higher performance observed for episodes involving concurrent depression and anxiety escalation (F1 = 0.84) and for more pronounced symptom changes (>=10-point increases, F1 = 0.85). Model interpretability was supported by SHAP-based analysis, which identified resting heart rate as the most influential feature in 71.4 percentage of detected anomalies, followed by physical activity and sleep. Together, our findings highlight the potential of explainable anomaly detection to enable personalized, scalable, and proactive mental health monitoring in real-world settings.
Performance models of interaction, such as Fitts Law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the
We draw on the predictive processing theory of perception to explain why healthy, intelligent, honest, and psychologically normal people might easily misperceive lights in the sky as threatening or extraordinary objects, especially in the context of WEIRD (western, educated, industrial, rich, and democratic) societies. We argue that the uniquely sparse properties of skyborne and celestial stimuli make it difficult for an observer to update prior beliefs, which can be easily fit to observed lights. Moreover, we hypothesize that humans have likely evolved to perceive the sky and its perceived contents as deeply meaningful. Finally, we briefly discuss the possible role of generalized distrust in scientific institutions and ultimately argue for the importance of astronomy education for producing a society with prior beliefs that support veridical perception.
Consider a setting where a pre-trained agent is operating in an environment and a human operator can decide to temporarily terminate its operation and take-over for some duration of time. These kind of scenarios are common in human-machine interactions, for example in autonomous driving, factory automation and healthcare. In these settings, we typically observe a trade-off between two extreme cases -- if no take-overs are allowed, then the agent might employ a sub-optimal, possibly dangerous policy. Alternatively, if there are too many take-overs, then the human has no confidence in the agent, greatly limiting its usefulness. In this paper, we formalize this setup and propose an explainability scheme to help optimize the number of human interventions.
Creativity assessment in science and engineering is increasingly based on both human and AI judgment, but the cognitive processes and biases behind these evaluations remain poorly understood. We conducted two experiments examining how including example solutions with ratings impact creativity evaluation, using a finegrained annotation protocol where raters were tasked with explaining their originality scores and rating for the facets of remoteness (whether the response is "far" from everyday ideas), uncommonness (whether the response is rare), and cleverness. In Study 1, we analyzed creativity ratings from 72 experts with formal science or engineering training, comparing those who received example solutions with ratings (example) to those who did not (no example). Computational text analysis revealed that, compared to experts with examples, no-example experts used more comparative language (e.g., "better/worse") and emphasized solution uncommonness, suggesting they may have relied more on memory retrieval for comparisons. In Study 2, parallel analyses with state-of-the-art LLMs revealed that models prioritized uncommonness and remoteness of ideas when rating originality, suggesting an evaluative process rooted around the semantic similarity of ideas. In the example condition, while LLM accuracy in predicting the true originality scores improved, the correlations of remoteness, uncommonness, and cleverness with originality also increased substantially -- to upwards of $0.99$ -- suggesting a homogenization in the LLMs evaluation of the individual facets. These findings highlight important implications for how humans and AI reason about creativity and suggest diverging preferences for what different populations prioritize when rating.
Advancements in generative Artificial Intelligence (AI) hold great promise for automating radiology workflows, yet challenges in interpretability and reliability hinder clinical adoption. This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system to bridge AI performance with clinical explainability. CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification. Meanwhile, the RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports. Our demonstration showcases the system's ability to deliver interpretable predictions, mitigate hallucinations, and generate high-quality, tailored reports with an interactive interface addressing accuracy, trust, and usability challenges. This framework provides a pathway to improving diagnostic consistency and empowering radiologists with actionable insights.
The dynamic nature of human health and comfort calls for adaptive systems that respond to individual physiological needs in real time. This paper presents an AI-enhanced digital twin framework that integrates biometric signals, specifically electrocardiogram (ECG) data, with environmental parameters such as temperature, humidity, and ventilation. Leveraging IoT-enabled sensors and biometric monitoring devices, the system continuously acquires, synchronises, and preprocesses multimodal data streams to construct a responsive virtual replica of the physical environment. To validate this framework, a detailed case study is conducted using the MIT-BIH noise stress test dataset. ECG signals are filtered and segmented using dynamic sliding windows, followed by extracting heart rate variability (HRV) features such as SDNN, BPM, QTc, and LF/HF ratio. Relative deviation metrics are computed against clean baselines to quantify stress responses. A random forest classifier is trained to predict stress levels across five categories, and Shapley Additive exPlanations (SHAP) is used to interpret model behaviour and identify key contributing features. These predictions are mapped to a structured set of environmental interventions using a Five Level Stress Intervention Mapping, which activates multi-scale responses across personal, room, building, and landscape levels. This integration of physiological insight, explainable AI, and adaptive control establishes a new paradigm for health-responsive built environments. It lays the foundation for the future development of intelligent, personalised healing spaces.
Evaluating text summarization quality remains a critical challenge in Natural Language Processing. Current approaches face a trade-off between performance and interpretability. We present SEval-Ex, a framework that bridges this gap by decomposing summarization evaluation into atomic statements, enabling both high performance and explainability. SEval-Ex employs a two-stage pipeline: first extracting atomic statements from text source and summary using LLM, then a matching between generated statements. Unlike existing approaches that provide only summary-level scores, our method generates detailed evidence for its decisions through statement-level alignments. Experiments on the SummEval benchmark demonstrate that SEval-Ex achieves state-of-the-art performance with 0.580 correlation on consistency with human consistency judgments, surpassing GPT-4 based evaluators (0.521) while maintaining interpretability. Finally, our framework shows robustness against hallucination.
The development of autonomous agents has seen a revival of enthusiasm due to the emergence of LLMs, such as GPT-4o. Deploying these agents in environments where they coexist with humans (e.g., as domestic assistants) requires special attention to trustworthiness and explainability. However, the use of LLMs and other deep learning models still does not resolve these key issues. Deep learning systems may hallucinate, be unable to justify their decisions as black boxes, or perform badly on unseen scenarios. In this work, we propose the use of s(CASP), a goal-directed common sense reasoner based on Answer Set Programming, to break down the high-level tasks of an autonomous agent into mid-level instructions while justifying the selection of these instructions. To validate its use in real applications we present a framework that integrates the reasoner into the VirtualHome simulator and compares its accuracy with GPT-4o, running some of the real use cases available in the domestic environments of VirtualHome. Additionally, since experiments with VirtualHome have shown the need to reduce the response time (which increases as the agent's decision space grows), we have proposed and evaluated a series of optimizations based on program analysis that exploit the advantages of the top-down execution of s(CASP).
Behind a set of rules in Deontic Defeasible Logic, there is a mapping process of normative background fragments. This process goes from text to rules and implicitly encompasses an explanation of the coded fragments. In this paper we deliver a methodology for \textit{legal coding} that starts with a fragment and goes onto a set of Deontic Defeasible Logic rules, involving a set of \textit{scenarios} to test the correctness of the coded fragments. The methodology is illustrated by the coding process of an example text. We then show the results of a series of experiments conducted with humans encoding a variety of normative backgrounds and corresponding cases in which we have measured the efforts made in the coding process, as related to some measurable features. To process these examples, a recently developed technology, Houdini, that allows reasoning in Deontic Defeasible Logic, has been employed. Finally we provide a technique to forecast time required in coding, that depends on factors such as knowledge of the legal domain, knowledge of the coding processes, length of the text, and a measure of \textit{depth} that refers to the length of the paths of legal references.
Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.
In the rapidly evolving educational landscape, the unbiased assessment of soft skills is a significant challenge, particularly in higher education. This paper presents a fuzzy logic approach that employs a Granular Linguistic Model of Phenomena integrated with multimodal analysis to evaluate soft skills in undergraduate students. By leveraging computational perceptions, this approach enables a structured breakdown of complex soft skill expressions, capturing nuanced behaviours with high granularity and addressing their inherent uncertainties, thereby enhancing interpretability and reliability. Experiments were conducted with undergraduate students using a developed tool that assesses soft skills such as decision-making, communication, and creativity. This tool identifies and quantifies subtle aspects of human interaction, such as facial expressions and gesture recognition. The findings reveal that the framework effectively consolidates multiple data inputs to produce meaningful and consistent assessments of soft skills, showing that integrating multiple modalities into the evaluation process significantly improves the quality of soft skills scores, making the assessment work transparent and understandable to educational stakeholders.
The Integrated Information Theory (IIT) might be our current best bet at a scientific explanation of phenomenal consciousness. IIT focuses on the distinctively subjective and phenomenological aspects of conscious experience. Currently, it offers the fundaments of a formal account, but future developments shall explain the qualitative structures of every possible conscious experience. But this ambitious project is hindered by one fundamental limitation. IIT fails to acknowledge the crucial roles of attention in generating phenomenally conscious experience and shaping its contents. Here, we argue that IIT urgently needs an account of attention. Without this account, IIT cannot explain important informational differences between different kinds of experiences. Furthermore, though some IIT proponents celebratedly endorse a double dissociation between consciousness and attention, close analysis reveals that such as dissociation is in fact incompatible with IIT. Notably, the issues we raise for IIT will likely arise for many internalist theories of conscious contents in philosophy, especially theories with primitivist inclinations. Our arguments also extend to the recently popularized structuralist approaches. Overall, our discussion highlights how considerations about attention are indispensable for scientific as well as philosophical theorizing about conscious experience.