Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive com...
Automatic Drum Transcription (ADT) remains challenging due to overlapping spectral content among drum instruments, dense transient activity, and the increased difficulty associated with fine-grained full-kit transcription. This study investigates whe...
Benefiting extensively from the Internet of Things (IoT) and sensor network technologies, the modern smart building achieves thermal comfort. It prevents energy wastage by performing automatic Fault Detection and Diagnosis (FDD) to maintain the good...
Aspect-based sentiment analysis (ABSA) is a crucial fine-grained sentiment analysis task that aims to determine sentiment polarity in a specific aspect term. Recent research has advanced prediction accuracy by pre-training models on ABSA tasks. Howev...
The General Data Protection Regulation (GDPR), adopted by the European Union in 2018, aims to enhance consumer trust and market efficiency by strengthening data protection. The concurrent stringent compliance requirements raise operational costs and...
Bimodal emotion recognition based on audio and text is widely adopted in video-constrained real-world applications such as call centers and voice assistants. However, existing systems suffer from limited cross-domain generalization and monolingual bi...
Smart city environmental monitoring depends on sparse air quality sensor networks and analytics services that remain reliable under node additions, outages, and missing streams. We propose an operational deep learning framework for citywide cross-loc...
Accurate short-term building energy consumption forecasting is crucial for energy-system operational efficiency and demand-side management. Existing deep learning models heavily rely on historical data, making them costly for data-scarce buildings. F...
Automatic building extraction from high-resolution imagery remains constrained by limited training data and domain shifts across geographic regions and spatial resolutions. Although data augmentation is widely applied in semantic segmentation, its ca...
Graph-to-text generation (G2T) involves converting structured graph data into natural language text, a task made challenging by the need for encoders to capture the entities and their relationships within the graph effectively. While transformer-base...
Smoothing is one of the fundamental procedures in functional data analysis (FDA). The smoothing parameter
With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed, coordinated, and intertwined across heterogeneous data modalities, posing substantial challenges to data-driven...
Traffic noise monitoring increasingly relies on automated vehicle type classification from audio recordings for source-specific noise analysis. However, training acoustic classifiers requires large, labeled datasets that are costly to collect. This s...
Accurate identification of urban functional zones is critically important for urban planning and sustainable development. However, existing deep learning methods exhibit limited cross-city generalization capabilities, rendering them inadequate for la...
Retrieval-augmented generation (RAG) across independent data providers requires local data custody, verifiable evidence handling, and fair compensation for contributed knowledge. We present BC-MultiRAG, a permissioned framework in which providers ret...
Automatic detection of sensitive fields in structured data is a critical prerequisite for privacy compliance and data governance. However, existing approaches face severe cross-domain generalization challenges. Hand-crafted pattern rules often fail u...
This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation...
We introduce a One-Shot Learning system where a robot effectively learns how to manipulate objects by relying solely on the object’s name, a single image, and a visual example of a person picking it up. Once the robot has mastered picking up a...
Accurate retrieval of Chlorophyll-a (Chla, mg m−3) from ocean color reflectance remains a challenging issue due to spectral redundancy, nonlinear optical interactions, and water type variability. This study develops and evaluates a globally rep...
Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of...
The Water Cloud Model (WCM) is the most widely used vegetation-scattering correction framework for microwave soil-moisture (SM) remote sensing, yet its cross-site generalization ability remains poorly understood. This study compares WCM-based random...
Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regardi...
We investigated the generation of proton- and alpha-induced nuclear cross-section data in the production of Indium-111 (111In) for application in nuclear medicine. Here, we are interested in three reaction channels, which are 109Ag (α, 2n), 111Cd (p,...
(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretabl...
The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration’s (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this databas...
We report recent enhancements to the online atomic database at the Ohio State University, NORAD-Atomic-Data, that provide various parameters for radiative and collisional atomic processes dominant in astrophysical plasma. NORAD stands for Nahar Osu R...
Against the background of highly interconnected global capital markets and rapidly propagating cross-lingual information streams, traditional anomaly detection paradigms based solely on single-modality numerical time-series sensors are insufficient f...
Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologi...
Cross-sectional energy benchmarking in the building domain has become crucial for policymakers, energy managers and property owners as they can compare an immovable property performance against its closest peers. For this, Key Performance Indicators...
The scarcity of labeled training samples poses a significant challenge in hyperspectral image classification. Cross-scene classification has been shown to be an effective approach to tackle the problem of limited sample learning. This paper investiga...
Autonomous vehicle (AV) technologies have advanced rapidly in recent years, driving an increasing demand for large-scale, high-quality annotated data. However, collecting and annotating real-world pedestrian video datasets is time-consuming, costly,...
The complex, heterogeneous nature of many subsurface environments makes accurate Rate of Penetration (ROP) prediction both critical and challenging for achieving drilling efficiency, cost control, and operational safety. Although artificial intellige...
Robot-centric elevation maps built from onboard sensing are always incomplete: occlusions, a limited field of view, and range limits leave large unobserved regions that traversability analysis and motion planning must still reason about. We present a...
Deploying accurate deep learning models for retinal microvascular segmentation on resource-constrained devices requires a trade-off between diagnostic precision and computational efficiency. Furthermore, evaluations based on a single fixed data split...
Ultralight accidents are reported to be more severe compared to those in other categories of sports aviation. In the absence of denominator data in the United States (US) but addressing a continuing concern in general aviation safety, this study give...
Transformer-based fake news classifiers often perform well within their training domain but show substantial performance degradation when applied to a different domain. This degradation is commonly attributed to changes in learned representations, wh...
Machine learning (ML) detectors of web and application programming interface (API) attacks report near-perfect laboratory performance but overwhelm analysts with false alerts in production. We argue analytically and support experimentally that when t...
Early-warning systems based on Learning Management System (LMS) traces are commonly evaluated using random or unseen-student splits, even when deployment requires predictions in courses that were absent during model development. In this study, we dis...
This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion...
Recent single-cell experiments that measure copy numbers of over 40 proteins in thousands of individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over...
Automated building damage assessment from satellite imagery has become increasingly critical for rapid disaster response and humanitarian relief operations. However, current state-of-the-art deep learning models exhibit significant generalization cha...
The diagnosis of skin cancer remains a growing challenge because of its high variability as a result of the varying imaging conditions in clinical settings. This paper proposes a multimodal deep learning framework to address these challenges by combi...
Background/Objectives: Accurate land cover mapping from multispectral Sentinel-2 imagery is fundamental for environmental monitoring, efficient natural resource management, and spatial planning. While deep learning has become the dominant approach fo...
Accurate state-of-health (SOH) estimation is essential for the safe, reliable, and cost-effective operation of lithium-ion battery systems. However, practical SOH modeling remains challenging because battery degradation data are often limited, degrad...
11 July 2025
Patient-generated health data (PGHD) enhance traditional healthcare by enabling continuous monitoring and supporting personalized care, yet concerns over privacy, security, and integration into existing systems hinder broader adoption. This study exa...
From global monitoring to regional forest management there is an increasing demand for information about forest ecosystems. For border regions that are closely connected ecologically and economically, a key factor is the cross-border availability and...
The inversion of potential field data has widely utilized the generalized cross-validation (GCV) and the unbiased predictive risk estimator (UPRE) methods to determine the regularization parameter. However, these two methods are time-consuming and it...
Background: The escalating burden of antimicrobial resistance (AMR) poses a critical threat to public health in Pakistan, with rates of high antibiotic consumption and limited standardized surveillance on AMR rates. Our study aimed to carry out a mul...
In Poland, drotaverine is the most frequently purchased antispasmodic, yet there is a paucity of real-world data on its use. We evaluated the profiles of patients who used drotaverine, and we investigated prescription patterns among general practitio...
The New Generation Productive Capacity Index (PCI) and the E-Government Development Index (EGDI) are increasingly used in various economies. Therefore, can e-government contribute to the continuous development of the new generation of productive capa...
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