To address statistical heterogeneity and update-level privacy risks in federated learning for geospatial data, this paper proposes a hierarchically decoupled collaborative framework that integrates client-side privacy perturbation with server-side co...
Sentiment classification often relies on costly in-domain annotations, while weak-supervision sources provide low-cost but inconsistent labels. We propose Multi-Source Reliability-Aware Aggregation and Cross-domain Consistency Learning (MSRA-CCL) for...
Electronic toll collection (ETC) gantries generate transaction records, yet existing anomaly-detection approaches often depend on manual labels or external information, limiting multiview inconsistency ranking under restricted supervision. We propose...
In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context...
Non-intrusive load monitoring (NILM) aims to estimate appliance-level power consumption from aggregate household measurements and provides an effective solution for fine-grained energy management. However, multi-appliance NILM remains challenging bec...
Federated learning (FL) is a promising paradigm for privacy-preserving IoT intrusion detection, but its effectiveness can be substantially degraded by the combination of heterogeneous non-IID client distributions and severe multi-class imbalance. Und...
Implicit Neural Representations (INRs) provide a flexible and resolution-independent formulation for continuous signal representation. Despite their strong representation ability, standard INRs usually predict each queried coordinate independently, m...
Cross-view geo-localization (CVGL) aims to match ground-level images with geo-tagged aerial imagery for precise localization, but remains challenging due to severe viewpoint discrepancies, partial correspondence, and significant domain shifts across...
Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent conne...
1 September 2026
Battery-powered IoT sensor nodes require energy-efficient Medium Access Control (MAC) protocols to extend network lifetime while supporting priority-sensitive traffic. This paper proposes a Priority-Aware Packet Aggregation/Piggybacking-Based (Priori...
The rapid expansion of Internet of Things (IoT) ecosystems has intensified the need for distributed intelligence mechanisms that reduce direct raw-data exposure while remaining resilient to adversarial manipulation. Federated learning (FL) addresses...
Water quality prediction in non-stationary environmental systems requires not only high predictive accuracy but also structural robustness under physical, ecological, and operational constraints. This study reframes multi-model fusion as a constraint...
Instance segmentation of 3D point clouds is a fundamental task for scene understanding in applications such as autonomous driving, robotics, and augmented reality. The inherent irregularity and sparsity of point clouds, compounded by scale variations...
18 March 2015
Disability organisations have not been engaged in the debate about the International Classification of Functioning, Disability and Health (ICF) in Sweden. We wanted to know representatives’ attitudes about the ICF. The aim of the study was to elucid...
Business risk assessment is a critical component of effective decision making, as it requires the systematic evaluation of multiple alternatives under diverse and often uncertain criteria. With the rapid growth of complex and heterogeneous business d...
The sparse and uneven distribution of point clouds in LiDAR-captured outdoor scenes poses significant challenges for 3D object detection in autonomous driving. Specifically, the imbalance between foreground and background points can degrade detection...
Multi-station sensing telemetry typically arrives out-of-order at the Open RAN (O-RAN) Near-RT RIC due to non-deterministic jitter in cloud-native protocol stacks, inducing a “temporal scrambling” effect that invalidates traditional spati...
Automatic building extraction plays an important role in various remote sensing applications, such as seismic disaster investigation, seismic hazard risk assessment, urban planning, and photogrammetry. Despite the substantial progress, state-of-the-a...
Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limi...
The massive expansion of Distributed Energy Resources and schedulable loads have forced a variation of generation, transmission, and final usage of electricity towards the paradigm of Smart Communities microgrids and of Renewable Energy Communities....
In regression-based 2D human pose estimation, accurate keypoint localization in crowded and occluded scenes remains challenging due to insufficient modeling of structural dependencies among joints. To address this issue, this paper proposes MACS-Pose...
Confidential ledgers cannot usually be pooled across entities, yet generic private federated learning does not preserve the identities that make accounting data meaningful. This paper develops Balance-Aware Federated Analytics (BAFA), a constrained f...
An 11-year-old American boy was staying with his family in Indonesia. He presented with a 5-month history of recurrent bruises and ecchymosis. A clinical diagnosis of acquired platelet dysfunction with eosinophilia was made when his full blood counts...
In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are
Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when...
Reliable ship detection in complex maritime optical imagery is a fundamental requirement for intelligent maritime monitoring and maritime automation systems. However, severe image degradation, large-scale variations, and background clutter often lead...
Visible–thermal (RGB-T) object detection improves robustness in complex environments, such as low-light and densely occluded forest scenes, by integrating complementary information from the two modalities. It has therefore become a key technolo...
Dynamic multi-criteria evaluation plays a central role in assessing complex systems based on panel data. However, existing objective weighting methods often suffer from sensitivity to extreme observations and may produce unstable or even negative wei...
This paper proposes a trust-aware environmental state consensus framework for smart agriculture that integrates TEE-enabled sensing, Byzantine-resilient aggregation, and lightweight blockchain-based state coordination under resource-constrained IoT e...
High-resolution remote sensing (RS) images exhibit complex backgrounds, large intra-class variability, and low inter-class differences, posing substantial challenges for semantic segmentation. Although existing class-level contextual modeling methods...
Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesio...
Virtual power plants (VPPs) use participant-reported capacity before response delivery to set internal compensation standards and participant-level response data afterward to allocate payments. Manipulation of these chronologically distinct data stre...
In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various...
Semantic segmentation is essential for fine-grained land-cover interpretation from high-resolution remote sensing imagery. However, existing methods still show limitations in coordinating multi-stage encoder features, providing scale-aware contextual...
Public opinion on online platforms now plays an important role in agricultural markets, which have always been unpredictable. Although sentiment analysis has been widely applied to agricultural texts, most existing studies typically focus only on cla...
Stereo foundation models provide strong cross-domain generalization but remain computationally expensive for real-time and resource-constrained deployment. We propose BRD-Stereo, a bilateral reliability distillation framework that transfers foundatio...
Multi-label text feature selection aims to identify compact and discriminative feature subsets for documents associated with multiple correlated labels. In federated environments, this task is further complicated by high-dimensional search spaces, no...
Cross-view re-identification of Hanwoo cattle remains challenging: weak texture provides only limited identity cues, and large pose and viewpoint changes easily distort these cues, causing severe feature dispersion. As a result, samples from the same...
Virtual Reality (VR) has emerged as a powerful medium for immersive human–computer interaction, where users’ emotional and experiential states play a pivotal role in shaping engagement and perception. However, existing affective computing...
Multimedia recommenders can use behavioral records together with visual and textual item information, but unreliable interactions and sparse histories still make user preference modeling difficult. Most graph-based methods propagate messages over obs...
The wealth of data and the enhanced computation capabilities of Internet of Vehicles (IoV) enable the optimized motion control of vehicles passing through an intersection without traffic lights. However, more intersections and demands for privacy pro...
Knowledge graphs (KGs) have gained prominence for representing real-world facts, with queries of KGs being crucial for their application. Aggregate queries, as one of the most important parts of KG queries (e.g., “ What is the average price of...
Accurate forecasting of copper futures prices is crucial for risk management and investment decisions. However, existing approaches primarily rely on historical prices and incorporate behavioral signals without a unified modeling framework. To addres...
Unmanned aerial vehicle (UAV) fault diagnosis is critical for ensuring flight safety, yet existing methods face challenges from class-imbalanced datasets and the lack of reliable uncertainty estimates. We address these issues with HCUNet, a hierarchi...
Wireless sensor networks (WSNs) play a critical role in cyber-physical applications such as industrial monitoring, environmental sensing, and critical infrastructure management. In these environments, security mechanisms must not only detect maliciou...
Accurate battery state-of-charge (SOC) estimation under dynamic operating conditions remains challenging because battery responses are nonlinear, history-dependent, temperature-sensitive, and prone to transient disturbances. To address this problem,...
Background: Dyspareunia is a common female sexual pain disorder that significantly impairs quality of life. Despite the availability of evidence-based treatments, including multimodal pelvic floor physiotherapy and psychosexual interventions, patient...
This article describes a pilot study to test the adequacy of a newly developed tool for an awareness plan on the importance of properly using pharmaceuticals. The new tool consists of face-to-face interviews with adult citizens on their approach to t...
This paper analyses China’s energy consumption and economic growth spillover effects on four world regions: (i) America (North and South); (ii) Europe and Central Asia; (iii) Asia Pacific; and (iv) Africa and the Middle East. An annual aggregat...
Few-Shot Semantic Segmentation (FSS) faces significant challenges in modeling complex backgrounds and maintaining prediction consistency due to limited training samples. Existing methods oversimplify backgrounds as single negative classes and rely so...
of 7