Search Results (1,215)

Urban Internet-of-Things (IoT) infrastructures increasingly host distributed artificial intelligence workloads across heterogeneous edge environments, supporting applications such as urban monitoring, resource optimization, and large-scale sensing analytics. Despite the rapid adoption of edge AI, current systems lack a unified architectural layer responsible for orchestrating distributed intelligence across dynamic urban infrastructures. Existing orchestration mechanisms are typically rule-based and rely on low-level telemetry signals such as latency, node availability, or network conditions, limiting their ability to interpret the contextual meaning of system behavior and environmental events. This paper introduces Urban AI OS, a trustworthy LLM-based semantic control layer for distributed urban edge intelligence. The proposed architecture transforms heterogeneous telemetry and environmental signals into high-level semantic events through large language model reasoning, enabling context-aware orchestration decisions including adaptive topology management, workload coordination, and node role assignment. To address the reliability risks associated with LLM-assisted control, Urban AI OS employs confidence-gated execution with deterministic fallback policies, post-action monitoring, rollback, and auditable decision logging to constrain the operational impact of uncertain or erroneous LLM recommendations. By decoupling semantic reasoning from the data plane execution of AI workloads, Urban AI OS provides a model-agnostic framework for managing large-scale urban edge intelligence infrastructures. Experimental evaluation on a real-world urban IoT deployment with 50+ edge devices demonstrates that the proposed semantic operating layer improves system responsiveness by 35%, reduces unnecessary topology changes by 42%, and improves communication efficiency compared to conventional rule-based and adaptive threshold baselines, while maintaining confidence calibration through formal fallback mechanisms. Full article

Renewable energy and energy storage stations require frequent updates of monitoring and control logic across heterogeneous devices and changing operating strategies. This paper proposes a rule graph-based reference architecture that combines low-code logic configuration, graph–model semantic binding, and microservice-oriented functional decomposition. A component status matrix separates the target architecture from the implemented subset. The runnable subset comprises a minimal FastAPI backend, REST/WebSocket telemetry interfaces, an in-process queue, and stateful rule evaluators; gateway, authentication, external message bus, time-series database, visual editor, and industrial protocol services remain design-level elements. Beyond the original single-rule example, a priority-ordered multi-device rule is implemented for cooperative BESS dispatch, communication/topology blocking, low-SOC protection, frequency-based load shedding, backup request, and five-sample recovery release. Existing local network benchmarks are complemented by a 600-step software-in-the-loop trace with scripted telemetry fluctuations and communication quality faults and by 500 in-process ASGI timing samples at each of the four point levels. The trace produced no safety dispatch or protected device violations. P99 application path latency ranged from 1.1962 to 5.5287 ms, but one 75.3065 ms outlier exceeded a 50 ms reference deadline, demonstrating that the Windows/FastAPI path is not deterministic. No industrial controller, hardware-in-the-loop facility, field data, or engineer usability study was used. Accordingly, the paper makes no claim of industrial real-time readiness or measured development effort reduction. Full article

Domain-specific information systems often maintain their data model, rule base, and application infrastructure as separate artifacts, complicating maintenance and pre-deployment verification. This study investigates whether these artifacts can be unified in a verifiable ontology-to-code pipeline without changing the expected classifications. The proposed Model-Driven Architecture uses the Business Application Builder framework and a Web Ontology Language 2 Description Logic ontology to represent domain structure, classification rules, and generation metadata. HermiT verifies consistency, satisfiability, and subsumption under open-world semantics before code generation. The generator produces persistence, business-logic, data-transfer, and presentation layers, while the generated Java application evaluates stored records under closed-world semantics and resolves overlapping categories using ontology-declared priorities. In a Serbian research-evaluation case study, the generated system reproduced the M30 and M33 classifications of an established Jess implementation. An internal secondary experiment generated and executed a prenatal-diagnosis application; all six runtime classifications matched the HermiT entailments and expected outcomes. The public artifact independently reproduces the ontology-level experiments but excludes the proprietary generator and generated source code. The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies. No performance superiority is claimed. Full article

With the development of smart classrooms and digital learning platforms, multimodal learning behavior data provide a new sensing basis for understanding students’ cognitive states and psychological risk warnings. However, existing educational data mining methods mainly focus on performance prediction, dropout warning, or surface-level emotion recognition, while continuous and interpretable modeling of deeper cognitive biases and related psychological risks remains insufficient. To address this issue, we propose MLBS-Net, a multimodal learning behavior sensing network for teaching feedback that jointly models students’ textual expressions, behavioral sequences, classroom interactions, and psychological auxiliary signals. MLBS-Net integrates theory-guided textual cognitive bias encoding, temporal behavioral state modeling, and reliability-aware cross-modal fusion to capture psychologically interpretable cognitive patterns, characterize dynamic learning-state changes, and adaptively integrate multimodal information according to data quality and task contribution while providing interpretable feedback for teachers. Experimental results show that MLBS-Net achieves a Macro-F1 of 0.855 for cognitive bias recognition and an AUC of 0.891 for psychological risk warning, outperforming traditional machine learning, unimodal deep learning, and standard multimodal methods. Ablation results further support the effectiveness of theory-guided semantic encoding, temporal behavioral modeling, reliability estimation, and multitask learning. These findings demonstrate that MLBS-Net can jointly characterize cognitive biases and potential psychological risks from multisource learning behaviors, providing a feasible approach for learning-state sensing, risk warning, and interpretable teaching support in smart education. Full article

Change detection under Gaussian noise is challenging because noise perturbs spectral clustering and posterior inference. This study presents a scene-adaptive weakly supervised framework that combines self-supervised single-image restoration with posterior-probability change modeling. A channel-spatial attention aggregation network (CAANet) is optimized jointly from the observed bitemporal scene by masked reconstruction without clean-image targets. The observed and restored images are coupled in a restoration-guided fuzzy decomposition, after which a normalized context-sensitive Bayesian network converts soft signal evidence into land-cover posterior vectors. Their temporal displacement is measured in a normalized semantic evidence space. Experiments on five remote-sensing datasets use zero-mean Gaussian noise with variance v ∈ {0.01, 0.03, 0.05, 0.07, 0.09}. At v = 0.05, FCC_CAANet achieves OA values of 0.9280–0.9659 and Kappa values of 0.7296–0.9521, obtaining the highest OA and Kappa on all five datasets among the implemented methods. The claims are limited to this controlled synthetic Gaussian-noise setting. Full article

Cross-view geo-localization (CVGL) is a critical technology used in unmanned aerial vehicles (UAVs) and widely applied in navigation and target localization tasks. However, owing to the extreme perspective disparity between UAV oblique views and satellite vertical views, CVGL still involves significant challenges, including geometric distortion caused by viewpoint differences, drastic appearance inconsistencies, and the difficulty in bridging semantic gaps between heterogeneous data. To address these issues, we propose a novel CVGL method named dynamic-feature collaborative optimization and semantic-alignment network (DFSA), designed to extract robust feature representations and achieve fine-grained alignment. Specifically, the DFSA employs a residual-based vision transformer as the backbone to capture global context while alleviating the training instability and feature collapse often associated with standard transformers. To bridge the semantic gap between global and local features, we design a feature optimization module comprising a local feature enhancer and a global feature aggregator. This module establishes a closed-loop collaborative system that facilitates top-down semantic guidance and bottom-up detail feedback. Furthermore, we introduce a semantic segmentation and alignment module that adaptively partitions images into semantic regions based on feature response distributions, shifting the matching granularity from the global level to the semantic region level to effectively overcome feature mismatches caused by positional offsets and scale variations. Extensive experiments conducted on the University-1652 and SUES-200 datasets demonstrate the superior image retrieval performance of the proposed DFSA. Specifically, DFSA achieves a Recall@1 of 94.87% and an Average Precision (AP) of 95.32% on the University-1652 dataset and maintains highly competitive Recall@1 performances between 96.83% and 99.25% across various altitudes on the SUES-200 dataset. These results validate the model’s effectiveness in handling extreme viewpoint changes for UAV-based cross-view image retrieval tasks. Full article

Vision–language foundation models such as CLIP are increasingly used for zero-shot behavior recognition, yet their robustness to prompt variations remains poorly understood. This paper investigates prompt sensitivity as a critical robustness concern in CLIP-family models for zero-shot classroom behavior analysis, treating prompt wording as a controlled semantic perturbation. Five representative vision–language models (CLIP/OpenAI, OpenCLIP/LAION, SigLIP2, EVA02-CLIP, and DFN-CLIP) are evaluated on three public classroom behavior benchmarks under a strict symmetric protocol. We compare four generic prompt strategies with a training-free Class-Aware Prompt Ensemble (CAPE). Results show that minor prompt changes can cause catastrophic performance degradation. On SigLIP2, an alternative wording of CAPE reduces Hit@1 on TeacherBehavior from 85.5% to 31.4%, a 54.1 percentage-point drop that exceeds the differences between model backbones. Across all five models, action-oriented prompts improve Hit@1 by up to 54 percentage points compared with label-only prompts. We further demonstrate that the apparent superiority of zero-shot CLIP over supervised linear probes largely arises from metric asymmetry. While zero-shot methods achieve higher Hit@1, they consistently underperform linear probes in multi-label evaluation (Sample-F1: 60–66% vs. 88–90%; Macro-F1: 49–60% vs. 68–78%). Bootstrap confidence intervals and paired-bootstrap significance tests further show that several reported performance differences are not statistically significant. These findings reveal prompt sensitivity as a fundamental deployment risk for vision–language foundation models in domain-specific behavior analysis. Prompt variations involving only a few words can silently undermine recognition performance while remaining hidden by conventional evaluation metrics. We therefore recommend that future benchmark studies report prompt configurations, multi-label F1 scores, and uncertainty estimates alongside headline Hit@1 to provide a more complete and reliable assessment of model capability. Full article

In psychotherapy, case formulation can organize clinically relevant information into a coherent account of how psychological difficulties emerge, persist, and may change. The Personal Meaning System Fuzzy Cognitive Map (PMS-FCM) represents the client’s bipolar construct system as a weighted directed graph, whereas the FCM-FRHP (Fuzzy Cognitive Map of Human Problem Formation and Resolution) provides a professional functional reference model for problem formation and resolution. This paper proposes a semantically constrained method for identifying systemic archetype-inspired configurations in PMS-FCM representations enriched with FCM-FRHP semantics. Rather than importing classical systemic archetypes directly, the method reformulates them as configurable graph templates adapted to intrapersonal bipolar construct systems. Detection combines FCM-FRHP functional roles, predefined semantic-affinity rules and PB-based structural criteria, edge-weight thresholds, and ranking criteria. The goal is to support the traceable identification of static structures that may inform the examination of clinically relevant systemic hypotheses, without treating them as diagnoses or evidence of observed temporal dynamics. The pipeline combines property-graph querying, RDF/SHACL conformance checking, ranked materialization, and rule-based trace generation. A local language model is used only after detection and conformance checking, as a constrained graph-to-text layer grounded in graph evidence and FCM-FRHP semantics. The approach offers a formally specified and reproducible method for conducting explicit, auditable pattern-level analysis of psychological case formulations. Full article

Remote sensing (RS) image change detection (CD) is crucial for environmental monitoring, urban planning, and disaster assessment. Despite recent advances, existing methods struggle to effectively exploit bitemporal difference information, leading to false alarms caused by illumination or seasonal variations. Furthermore, they often fail to fully leverage coarse predictions as explicit semantic priors, restricting their capability to detect small-scale change regions. To address these issues, we propose a Difference-Gated Interaction and Change-Aware Cross-Temporal Fusion Network (DCAFNet) within a unified coarse-to-fine framework. Specifically, a Difference-Gated Feature Interaction (DGFI) module generates change correlation gates based on difference magnitudes to suppress pseudo-changes while preserving genuine change signals, and a Change-Aware Cross-Temporal Fusion (CCTF) module leverages coarse predictions as semantic guidance for feature recalibration and employs cross-temporal attention with learnable adaptive fusion to recover subtle change regions. Extensive experiments on four benchmark datasets (LEVIR-CD, WHU-CD, CDD, and SYSU-CD) demonstrate that DCAFNet consistently outperforms ten state-of-the-art methods, with additional ablation studies confirming the effectiveness of each component. Full article

Limited by the diversity and complexity of real-world scenes, existing remote sensing change detection methods often suffer from insufficient fine-grained semantic understanding and blurred boundaries of change targets. To address these issues, this paper proposes a boundary-constrained remote sensing change detection network based on vision foundation models (BCNet). BCNet employs a differential modeling approach and multi-branch guidance mechanism to design a differential detail enhancement module, amplifying fine-grained semantic information. Through cross-layer feature alignment, stepwise fusion, and edge-sensitive modeling, it constructs a multi-scale edge enhancement module that enhances perception of minute variations and edge details, fully leveraging the universal semantic representation capabilities of the vision foundation model. In addition, an edge feature constraint mechanism is introduced that applies dual guidance and supervision during the feature fusion and output stages. This mechanism achieves refined delineation of change region boundaries and significantly mitigates the issue of boundary blurring. Experimental results on four mainstream datasets, namely LEVIR-CD, WHU-CD, NJDS and MSRS-CD, demonstrate that BCNet outperforms 13 state-of-the-art methods in terms of key metrics including F1 and IoU. Against the best VFM-based baseline, BCNet obtains F1 score gains of 0.21%, 0.71%, 6.33% and 0.63% on the above four datasets. Specifically, the proposed method exhibits superior detection accuracy and edge detail preservation capabilities in complex regions. Full article

Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized by multi-source heterogeneity, cross-scale associations and dynamic change. Existing knowledge organization approaches mainly rely on textual synthesis, indicator systems or general-purpose knowledge graph construction tools, and therefore struggle to simultaneously handle cross-context implicit relations, domain-rule constraints, inconsistent entity expressions and evidence traceability in ecological restoration knowledge. To address these limitations, this paper proposes EcoRestore-KG, a multi-agent knowledge graph construction framework for territorial ecological restoration. The framework unifies heterogeneous inputs through controlled evidence representation and adaptive context segmentation, and organizes ontology-guided triple mining, cross-context relation inference, graph quality control, entity canonicalization, relation endpoint remapping and evidence binding into a progressive workflow for the automatic extraction, auditing and assembly of ecological restoration knowledge. Experimental results show that EcoRestore-KG outperforms general-purpose large language models and existing knowledge graph construction baselines in relation extraction, entity coverage and semantic-quality evaluation. It achieves relation precision, recall and F1 scores of 71.4% ± 0.3%, 69.5% ± 4.4% and 70.3% ± 2.3%, respectively, improving relation F1 by 9.9 percentage points over the strongest baseline. Its entity F1 reaches 79.8% ± 0.7%, and its LLM-S score reaches 8.48 ± 0.11. Single-module and combined ablation experiments further demonstrate that evidence representation, context segmentation, cross-context relation inference, relation quality auditing and entity canonicalization jointly support the performance gains of the framework. This study provides a verifiable methodological pathway for structured organization, quality auditing, evidence tracing and subsequent integration of newly available knowledge. Full article

Frame-based RGB image sensors and event-based vision sensors provide complementary sensing capabilities for human-perceptual imaging and machine vision. RGB image sensors capture dense spatial, color, and texture information that is essential for human-viewable imaging, semantic recognition, and conventional image signal processing pipelines. In contrast, dynamic vision sensors (DVSs) and event vision sensors (EVSs) asynchronously detect local brightness changes and provide sparse temporal information with low latency, high temporal resolution, and reduced redundant data output. Because neither modality alone satisfies all requirements of emerging vision systems, hybrid event–frame sensing has become an important direction for compact, low-latency, and energy-efficient sensing. This review presents a sensor-oriented taxonomy of hybrid event–frame sensing architectures and systems, including dual-camera event–frame systems, optically aligned event–frame systems, pixel-level shared hybrid image sensors, stacked CIS–DVS hybrid image sensors, homogeneous-pixel sensing systems, and event-only reconstruction systems. We analyze key sensor specifications, including latency, spatial resolution, color fidelity, power consumption, and form factor, and discuss how these specifications guide sensor configuration and design. The review identifies stacked CIS–DVS sensors as one of the most balanced and competitive architectures because they can support compact integration, synchronized event–frame sensing, and on-chip processing. However, important challenges remain, including color fidelity, demosaicing, event-pixel ratio optimization, calibration, benchmarking, and edge-AI deployment. Finally, we emphasize that future hybrid event–frame sensing systems should be developed through sensor–algorithm–ISP–AI co-design. This review provides practical guidelines for developing next-generation hybrid event–frame sensing systems for both human-perceptual imaging and machine vision. Full article

In heterogeneous multi-robot systems, allocating tasks efficiently requires determining whether each robot can actually carry out a given task. In existing multi-robot task allocation research, however, such task feasibility has typically been handled inside a particular optimizer or symbolic planner, while spatial traversability has been assessed against static criteria that cannot capture the changes induced by a robot’s loaded state. This paper proposes an ontology-based semantic feasibility reasoning method for heterogeneous multi-robot task allocation. The proposed method defines semantic models for robots, tasks, and places and applies hybrid reasoning, combining declarative reasoning with procedural evaluation, to determine multi-axis capability conditions and loaded-state place reachability. The reasoning result is formalized as an allocator-independent ReasonerOutput that serves as a common input for diverse allocation algorithms. In experiments spanning four scenarios over three fleet configurations and four allocators, together with an ablation study on 200 randomized instances at each of three problem scales, the proposed ReasonerOutput consistently functioned as a shared semantic feasibility constraint. The experiments further showed that both the fleet composition and the loaded state of the target item affect assignment feasibility. These results indicate that, for the static one-shot assignment setting evaluated here, the proposed method makes the task feasibility of heterogeneous robots explicit through semantic reasoning and allows allocators of differing algorithmic character to draw on this feasibility in common. Full article

Cross-scene hyperspectral image (HSI) classification seeks to learn a classifier from annotated source-scene data and deploy it on unlabeled scenes whose imaging conditions and data distributions differ from those seen during training. Existing cross-scene learning strategies mainly include domain adaptation (DA) and domain generalization (DG). In practical scenarios, target-domain samples are commonly unknown, inaccessible, or time-varying before deployment. DG is a more practical choice for such applications. Yet, single-source DG still faces a key difficulty: expanded samples must contain meaningful domain changes without corrupting class semantics. If the generated domains are weak or deviate from their original categories, the classifier may learn unstable or misleading cues. Therefore, we introduce the View-Consistent Domain Calibration Network (VDCnet), which is designed to improve the quality and training value of generated samples for single-source cross-scene classification. VDCnet consists of a Calibrated Expansion Generator (CEG) and a View Semantic Matching Mechanism (VSM). CEG performs reliability-gated spectral-spatial residual perturbation to produce semantically trustworthy extended samples, rather than simply enlarging the sample set. VSM further enforces multi-view semantic consistency in both prediction distributions and projected feature representations, promoting diverse feature learning while suppressing semantic drift. Experiments on the Houston, Pavia, and Shanghai–Hangzhou datasets demonstrate that VDCnet improves overall accuracy over the leading DG methods by 1.29, 2.65, and 1.26 percentage points, respectively, indicating its superior performance. Full article

Open-pit mines contain rapidly changing terrain, discontinuous bench structures, and mixed artificial–natural objects, which complicate automated three-dimensional mapping. This study presents a dual-module workflow for UAV LiDAR point clouds. Module A characterizes local geometry using normal and curvature descriptors, constructs local plane support through RANSAC fitting, and detects candidate bench-line points using an angular-gap criterion, followed by regional grouping and Kalman-filter refinement. Qualitative overlay with the orthophoto showed coherent correspondence with principal platform–slope transitions. Module B segments buildings, roads, and vegetation using a PointNet++ network enhanced by local Transformer self-attention and inverted residual feature transformation. Under a fixed spatial hold-out setting, the network achieved an overall accuracy of 97.6% and a mean intersection over union of 96.4%. It obtained the highest overall accuracy, mean intersection over union, and class-wise intersection over union among the selected baselines, whereas Point Transformer achieved a slightly higher mean class accuracy. The two independently operated modules provide complementary structural and semantic information for open-pit mine mapping. Broader applicability requires reference-based bench-line assessment and evaluation across additional mines and survey periods. Full article