Affective Image Stylization (AIS) converts an emotional intent into executable artistic visual styles. Existing methods are often limited to discrete label settings and provide limited interpretability of how target emotions are realized. We propose...
Temporal knowledge graphs (TKGs) effectively represent dynamic facts by incorporating a temporal dimension, yet they frequently encounter data incompleteness issues that constrain downstream applications. Concurrently, TKG prediction tasks, which ena...
Multimodal large language models (MLLMs) have been used to support dental image interpretation, but performance may depend on prompt structure. We proposed Hierarchical Reasoning Gates (HRG), a prompt-level protocol organizing image interpretation in...
The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or asse...
Large language models can generate fluent chain-of-thought (CoT) reasoning that appears coherent while exhibiting systematic distortions in evidence weighting and hypothesis comparison. This paper studies hallucinated CoT as a discourse-structural ph...
Accurate prediction of electric vehicle charging demand is imperative for ensuring grid stability and optimizing urban mobility resources. While the emergence of large language models has introduced translation-based forecasting paradigms, existing m...
Multi-Hop Knowledge Graph Question Answering (multi-hop KGQA) aims to obtain answers by analyzing the semantics of natural language questions and performing multi-step reasoning across multiple entities and relations in knowledge graphs. Traditional...
A temporal knowledge graph represents temporal information between entities in a multi-relational graph. Its reasoning aims to infer and predict potential links among entities. Predicting time-aware entities is a challenging task due to significant d...
Optical coherence tomography angiography (OCTA) is a depth-resolved, label-free optical imaging modality that uses motion contrast from repeated B-scans to reconstruct retinal microvasculature and provide co-registered en face views of the superficia...
Recent language models exhibit significant logical reasoning abilities, yet the mechanisms supporting deductive inference remain poorly understood. This paper studies small transformer-based language models trained from scratch on multi-step deductiv...
The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language model...
Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current stu...
Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and i...
Large language models (LLMs) are increasingly being used in education, yet their correctness alone does not capture the quality, reliability, or pedagogical validity of their problem-solving behavior, especially in mathematics, where multi-step logic...
While neural networks can solve complex geometric problems, as demonstrated by systems like AlphaGeometry, we have limited understanding of how they internally represent and reason about spatial relationships. In this work, we investigate how neural...
Accurate differential diagnosis of Kawasaki disease (KD) from other febrile conditions remains challenging owing to nonspecific clinical presentation, absence of validated biomarkers, and reliance on post hoc explainability methods that cannot suppor...
According to Dworkin, “truth” is an interpretative concept. Why? Moral judgements are often the subject of disagreement because they are often the result of divergent conceptual understandings. If, on the other hand, we want to interpret...
Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from...
Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is...
Large LNG storage tanks are key shore-based facilities linking maritime transport with receiving-terminal storage and send-out. Their continuous monitoring needs anomaly identification, preservation of physically meaningful deviation directions, and...
Background: Chest X-ray (CXR) is widely used for the assessment of thoracic diseases, yet automated multi-label interpretation remains challenging due to subtle visual patterns, overlapping anatomical structures, and frequent co-occurrence of abnorma...
Clinical reasoning over 3D CT scans is inherently compositional, requiring the integration of anatomical measurement, pathology assessment, spatial comparison, and clinical interpretation. We introduce MedToolica, a finetuning-free, role-based agenti...
Industrial robot severity diagnosis must combine evidence from coupled subsystems while remaining stable under fluctuating measurements. We develop an uncertainty-adaptive robust graph-based belief rule base (RG-BRB) that maps monitored attributes to...
Equitable and reasonable utilization is a core principle in the distribution of water resources. However, its practical application in the Nile Basin has been a significant challenge, leading to a water allocation deadlock. To address the deadlock, w...
Social media is in a dynamic environment of real-time interaction, and users generate overwhelming and high-dimensional information at all times. A new case-based reasoning (CBR) method combined with attribute features mining for posting-popularity p...
Large language models and the agents built on them can reach the same outcome through different internal computations, reasoning trajectories, and system-level policies. Existing reviews typically treat mechanistic interpretability, reasoning, agent...
The increasing demand for predictive maintenance in industrial environments requires edge-intelligent solutions in which latency, energy consumption, memory footprint, and data movement are strictly constrained. This paper presents a two-stage Edge A...
Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong p...
Quantum computing exploits the principles of quantum mechanics to perform computation. Information is stored in qubits and processed with a sequence of quantum gates arranged as circuits. Verifying the correctness of quantum circuits is becoming esse...
The Arctic Northeast Passage is of growing economic and strategic importance, while its harsh and highly variable navigation environment poses substantial challenges for vessel operation. Existing assessment approaches often rely on predefined struct...
Background: Millimetric ST-segment elevation (STEMI) rules miss more than half of angiographic coronary occlusions. Re-casting acute infarction as Occlusion MI (OMI) versus Non-Occlusion MI (NOMI) and embedding that paradigm in Bayesian reasoning cou...
The remarkable success of autoregressive Large Language Models (LLMs) is predicated on the causal attention mechanism, which enforces a static and rigid form of informational asymmetry by permitting each token to attend only to its predecessors. Whil...
The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based...
Texture plays an important role in computer vision in expressing the characteristics of a surface. Texture complexity evaluation is important for relying not only on the mathematical properties of the digital image, but also on human perception. Huma...
Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry...
Objective: Sepsis is a life-threatening response to infection and a major cause of hospital mortality. Machine learning (ML) models have demonstrated better sepsis prediction performance than integer risk scores but are less widely used in clinical s...
The prediction of stock price trends is of vital importance for maintaining the stability of the financial market, optimizing resource allocation and preventing systemic risks. To ensure the practical application value of the prediction model, it is...
The accuracy of self-perceptions and the maturity to handle feedback received from others are instrumental for one’s mental health, interpersonal relations, and effectiveness in the classroom and at work. Nonetheless, research on one’s co...
Autonomous vehicles need to explain their actions to improve reliability and build user trust. This study focuses on enhancing the transparency and explainability of the decision-making process in such systems. A module named XAI-Interpreter is devel...
Reliable prediction of chemical–protein interactions (CPIs) remains a key challenge in drug discovery, especially under sparse or noisy biological data. We present MM-TCoCPIn, a Multi-Modal Topology-aware Chemical–Protein Interaction Netw...
Symmetry and asymmetry appear together in aero-engine prognostics and health management. A monitoring system should preserve a common decision structure across operating scenarios, while sensor evidence becomes asymmetric under changing operating con...
As AI-generated humanoid characters are increasingly used in virtual, augmented, and mixed reality applications, evaluating the Uncanny Valley Effect (UVE) is crucial for immersive user experience. Existing evaluation methods map visual features to a...
We propose a post-hoc explanation layer for binary Quantum Logic Decision Tree classifiers constructed with QLDT+. The layer does not modify the fitted tree, the decision threshold, or the predicted label. It translates the class-labelled QLDT+ paths...
Background/Objectives: Fibromyalgia (FM) is a chronic pain condition characterized by widespread pain, fatigue, sleep disturbances, and psychosocial challenges. Although physiotherapists play a key role in FM management, little is known about how bio...
Background: Laryngeal and hypopharyngeal cancers (LHCs) exhibit heterogeneous outcomes after definitive radiotherapy (RT). Large language models (LLMs) may enhance prognostic stratification by integrating complex clinical and imaging data. This study...
Fine-grained species classification involves distinguishing visually similar taxa and remains challenging for general-purpose visual recognition models. We propose BioReason, a retrieve-then-reasonframework that uses BioCLIP candidates to formulate a...
As the vital technology of natural language understanding, sentence representation reasoning technology mainly focuses on sentence representation methods and reasoning models. Although the performance has been improved, there are still some problems,...
Open-set recognition of wild plants in natural complex scenes is an important task for plant conservation, ecological monitoring, and precision agriculture. Traditional closed-set learning methods struggle to handle unseen species not covered by the...
Understanding rate of change requires reasoning about measurable quantities and how one quantity changes relative to another. To support this kind of reasoning, teachers should develop the ability to notice students’ quantitative and covariatio...
In ontology engineering, reusing (or extending) ontologies poses a significant challenge, requiring revising their ontological commitments and ensuring accurate representation and coherent reasoning. This study aims to address two main objectives. Fi...
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