Search Results (104)

Multi-Object Tracking (MOT) is a fundamental research topic in the field of computer vision, with broad application potential in unmanned aerial vehicle (UAV) videos. However, existing methods still face significant challenges in detection discriminability and identity association stability due to the small scale and weak appearance of objects under aerial viewpoints, as well as complex background interference. To address these issues, we propose an Edge-Graph Enhanced Network (EGEN) for UAV aerial MOT, aiming to improve the performance of small object detection (SOD) and tracking in complex scenes. The framework follows a one-step tracking paradigm and consists of three main components: object detection, embedding feature extraction, and data association. In the detection stage, we design an Edge-Guided Gaussian Enhancement Module (EGGEM), which models edge relationships between objects and backgrounds from a global perspective and selectively enhances Gaussian features guided by edge information, thereby strengthening key structural features of small objects while suppressing background interference. In the embedding feature extraction stage, we develop a Graph-Guided Embedding Enhancement Module (GGEEM), which explicitly represents re-identification (ReID) embeddings as a graph structure and jointly models nodes and their neighborhood relationships to fully capture inter-object associations and enhance embedding discriminability. In the data association stage, we introduce a hierarchical two-stage association strategy to match objects with different confidence levels separately, improving tracking stability and robustness. Extensive experiments on the VisDrone, UAVDT, and self-constructed WildDrone datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in both SOD and MOT, demonstrating strong generalization and practical applicability. Full article

This study investigates the evolution of public sentiment and discourse surrounding artificial intelligence through a comprehensive multi-method analysis of 28,819 Reddit comments spanning March 2015 to May 2024. Addressing three research questions—(1) what dominant topics characterize AI discourse, (2) how has sentiment changed over time, particularly following ChatGPT 5.2’s release, and (3) what linguistic patterns distinguish positive from negative discourse—we employ 28 distinct analytical techniques to provide validated insights into public AI perception. Methodologically, the study integrates VADER sentiment analysis, Linguistic Inquiry and Word Count (LIWC) analysis with regression validation, dual topic modeling using Latent Dirichlet Allocation and Non-negative Matrix Factorization for cross-validation, four-dimensional tone analysis, named entity recognition, emotion detection, and advanced NLP techniques including sarcasm detection, stance classification, and toxicity analysis. A key methodological contribution is the validation of LIWC categories through linear regression (R² = 0.049, p < 0.001) and logistic regression (61% accuracy), moving beyond the descriptive statistics typical of prior linguistic analyses. Results reveal a pronounced decline in positive sentiment from +0.320 in 2015 to +0.053 in 2024. Contrary to expectations, sentiment decreased following ChatGPT’s November 2022 release, with negative comments increasing from 31.9% to 35.1%—suggesting that direct exposure to powerful AI capabilities intensifies rather than alleviates public concerns. LIWC regression analysis identified negative emotion words (β = −0.083) and positive emotion words (β = +0.063) as the strongest sentiment predictors, confirming that affective rather than technical engagement drives public AI attitudes. Topic modeling revealed nine coherent themes, with facial recognition, algorithmic bias, AI ethics, and social media misinformation emerging as dominant concerns across both LDA and NMF analyses. Network analysis identified regulation as a central hub (degree centrality = 0.929) connecting all major AI concerns, indicating strong public appetite for governance frameworks. These findings contribute to theoretical understandings of technology risk perception, provide practical guidance for AI developers and policymakers, and demonstrate validated computational methods for tracking public opinion toward emerging technologies. Full article

Social organizations increasingly rely on Natural Language Processing (NLP) to analyze large-scale textual data for high-stakes decisions, including university admissions, financial aid allocation, and job hiring. Current methods primarily employ topic modeling and sentiment analysis, but they fail to capture the complex ways emotions and cultural contexts shape meaning in text, potentially perpetuating bias and undermining equitable decision-making. To address this gap, we introduce the Behavioral and Emotional Theme Detection (BET) framework, a novel approach that integrates emotional, cultural, and sociological dimensions into topic detection and emotion analysis. By applying BET to English and Hebrew datasets, we showcase its multilingual adaptability and its potential to reveal rich thematic content and emotional resonance in biographical texts. Our results demonstrate that BET not only enhances the granularity and diversity of detected themes but also tracks shifts in emotional framing over time, offering deeper insights into how individuals deploy linguistic resources to position their identities, enabling more equitable assessment practices. Full article

While railways are critical for transportation, their expansive networks spanning thousands of kilometers pose significant challenges for conventional structural health inspection and maintenance. Recent advancements in sensors and artificial intelligence technologies have led to a substantial growth in the body of research proposing innovative approaches for Railway Track Structural Health Monitoring (RTSHM) to enhance safety and operational efficiency. This work aims to synthesize the current RTSHM research landscape to explore mainstream and emerging directions and identify advancements, challenges, and opportunities in this field. Through the hybrid systematic review using bibliometrics analysis and topic modeling, core research themes emerged, from developing sensor and data acquisition techniques as the foundation, to be combined with AI-based methods for fault detection and prediction. These predictions are leveraged for predictive maintenance through degradation modeling, supplemented with information from dynamic response assessment and performance optimization, and the ultimate goal is integration of RTSHM for operational safety assessments and risk-based decision-making. While technologically advanced, current research predominantly focuses on detecting discrete defects, thereby neglecting the holistic management of the track system. This fragmentation contributes to a complex and often siloed landscape for infrastructure management, emphasizing that RTSHM remains in a critical developmental stage. Consequently, the development of smart railway, integrated with intelligent data collection devices, deep learning technologies, and innovative operational platforms, represents a challenging yet promising direction for future research. These advancements are anticipated to foster safer, more efficient, and sustainable railway systems worldwide. Full article

This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders. Full article

Due to its extensive applications, aerial image object detection has long been a hot topic in computer vision. In recent years, advancements in unmanned aerial vehicle (UAV) technology have further propelled this field to new heights, giving rise to a broader range of application requirements. However, traditional UAV aerial object detection methods primarily focus on detecting predefined categories, which significantly limits their applicability. The advent of cross-modal text–image alignment (e.g., CLIP) has overcome this limitation, enabling open-vocabulary object detection (OVOD), which can identify previously unseen objects through natural language descriptions. This breakthrough significantly enhances the intelligence and autonomy of UAVs in aerial scene understanding. This paper presents a comprehensive survey of OVOD in the context of UAV aerial scenes. We begin by aligning the core principles of OVOD with the unique characteristics of UAV vision, setting the stage for a specialized discussion. Building on this foundation, we construct a systematic taxonomy that categorizes existing OVOD methods for aerial imagery and provides a comprehensive overview of the relevant datasets. This structured review enables us to critically dissect the key challenges and open problems at the intersection of these fields. Finally, based on this analysis, we outline promising future research directions and application prospects. This survey aims to provide a clear road map and a valuable reference for both newcomers and seasoned researchers, fostering innovation in this rapidly evolving domain. We keep track of related works in a public GitHub repository. Full article

Introduction: The figure-of-eight walk test (F8WT) assesses gait on a curved path, reflecting everyday walking complexity. Despite recognized validity among elderly individuals, its application in neurological disorders remains inadequately explored. This scoping review summarizes evidence regarding F8WT use, validity, and clinical applicability among individuals with neurological disorders. Methods: A systematic literature search was conducted in the PubMed, Scopus, Embase, and Web of Science databases. After reading the full text of the selected studies and applying predefined inclusion criteria, seven studies, involving participants with multiple sclerosis (n = 3 studies), Parkinson’s disease (n = 2 studies), and stroke (n = 2 studies), were included based on pertinence and relevance to the topic. Results: F8WT demonstrated strong reliability and validity across various neurological populations and correlated significantly with established measures of gait, balance, and disease severity. Preliminary evidence supports its ability to discriminate individuals at increased fall risk and detect subtle motor performance changes. Discussion: The F8WT emerges as a valuable tool, capturing multifaceted gait impairments often missed by linear walking assessments. Sensitive to subtle functional changes, it is suitable for tracking disease progression and intervention efficacy. Conclusions: F8WT is reliable and clinically relevant, effectively identifying subtle, complex walking impairments in neurological disorders. Full article

Multiple object tracking (MOT) is a critical and active research topic in computer vision, serving as a fundamental technique across various application domains such as human–robot interaction, autonomous driving, and surveillance. MOT typically consists of two key components: detection, which produces bounding boxes around objects, and association, which links current detections to existing tracks. Two main approaches have been proposed: one-shot and two-shot methods. While previous works have improved MOT systems in terms of both speed and accuracy, most works have focused primarily on enhancing association performance, often overlooking the impact of accelerating detection. Thus, we propose a high-speed MOT system that balances real-time performance, tracking accuracy, and robustness across diverse environments. Our system comprises two main components: (1) a hybrid tracking framework that integrates low-frequency deep learning-based detection with classical high-speed tracking, and (2) a detection label-based tracker management strategy. We evaluated our system in six scenarios using a high-speed camera and compared its performance against seven state-of-the-art (SOTA) two-shot MOT methods. Our system achieved up to 470 fps when tracking two objects, 243 fps with three objects, and 178 fps with four objects. In terms of tracking accuracy, our system achieved the highest MOTA, IDF1, and HOTA scores with high-accuracy detection. Even with low detection accuracy, it demonstrated the potential of long-term association for high-speed tracking, achieving comparable or better IDF1 scores. We hope that our multi-processing architecture contributes to the advancement of MOT research and serves as a practical and efficient baseline for systems involving multiple asynchronous modules. Full article

This document comprehensively analyses the literature on accelerometers used in monitoring systems designed for rail vehicle applications. It reviews the current research on this topic and highlights key findings, methodologies, and trends in the field. Additionally, it discusses the role of accelerometers in enhancing safety and performance within rail vehicle systems. This review is structured into several sections: Introduction, Fundamentals of Accelerometer Data, Signal-Processing Techniques, Examples of Accelerometers Used in Railway Monitoring Systems, and a Guide for Choosing the Right Accelerometer. One of the primary contributions of this paper is recommending the best accelerometer in terms of cost and performance for use in the rail vehicle industry. Future work will consider using an online detection tool for the acceleration of the frame of the railway coach and signalization of the peak values using the train intercom to the driver and static diagnosis systems. This approach aims to facilitate the detection of track irregularities, wind influence, and failures of the coach suspensions, which can be easily detected. Full article

Three-dimensional reconstruction using monocular camera images is a well-established research topic. While multi-image approaches like Structure from Motion produce sparse point clouds, single-image depth estimation via machine learning promises denser results. However, many models estimate relative depth, and even those providing metric depth often struggle with unseen data due to unfamiliar camera parameters or domain-specific challenges. Accurate metric 3D reconstruction is critical for railway applications, such as ensuring structural gauge clearance from vegetation to meet legal requirements. We propose a novel method to scale 3D point clouds using the track gauge, which typically only varies in very limited values between large areas or countries worldwide (e.g., $1.435$ m in Europe). Our approach leverages state-of-the-art image segmentation to detect rails and measure the track gauge from a train driver’s perspective. Additionally, we extend our method to estimate a reasonable railway-specific extrinsic camera calibration. Evaluations show that our method reduces the average Chamfer distance to LiDAR point clouds from $1.94$ m (benchmark UniDepth) to $0.41$ m for image-wise calibration and $0.71$ m for average calibration. Full article

Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. This article provides an overview of feature fusion’s development into feature-level fusion and proposal-level fusion, and it specifically reviews multiple related algorithms. We discuss the application of current multi-sensor object detection algorithms. In the future, with the continuous advancement of sensor technology and the development of artificial intelligence algorithms, multi-sensor fusion object detection will show great potential in more fields. Full article

Traditional text classification models predominantly rely on static text representations, failing to capture temporal variations in language usage and evolving semantic meanings. This limitation reduces their ability to accurately classify time-sensitive texts, where understanding context, detecting trends, and addressing semantic shifts over time are critical. This paper introduces a novel time-aware short text classification model incorporating temporal information, enabling tracking of and adaptation to evolving language semantics. The proposed model enhances contextual understanding by leveraging timestamps and significantly improves classification accuracy, particularly for time-sensitive applications such as News topic classification. The model employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, enriched with attention mechanisms to capture both local and global dependencies. To further refine semantic representation and mitigate the effects of semantic drift, the model fine-tunes GloVe embeddings and employs synonym-based data augmentation. The proposed approach is evaluated on three benchmark dynamic datasets, achieving superior performance with classification accuracy reaching 92% for the first two datasets and 85% for the third dataset. Furthermore, the model is applied to a different-fields categorization and trend analysis task, demonstrating its capability to capture temporal patterns and perform detailed trend analysis of domain-agnostic textual content. These results underscore the potential of the proposed framework to provide deeper insights into the evolving nature of language and its impact on short-text classification. This work advances natural language processing by offering a comprehensive time-aware classification framework, addressing the challenges of temporal dynamics in language semantics. Full article

Mental disorders (MDs) are among the major causes of morbidity and mortality worldwide. Individuals with severe MDs have a shorter life expectancy, primarily due to cardiovascular diseases. Echocardiography facilitates the evaluation of alterations in cardiac morphology and function, resulting from various cardiac pathologies. The aim of this review was to explore the current evidence base behind the myocardial deformation observed in echocardiography in patients with MDs. We primarily focused on the data regarding speckle tracking echocardiography. PubMed, using medical subject headings, was searched to identify studies on this topic. The collected data demonstrated changes in myocardial function in schizophrenia, bipolar disorder, depression, anxiety disorder, stressor-related disorder, post-traumatic stress disorder, eating disorders, sleep–wake disorders, substance-related and addictive disorders, neurocognitive disorders, and borderline personality disorder. The recurrent findings included impaired Left Ventricular Ejection Fraction and Left Ventricular Hypertrophy. Global Longitudinal Strain was significantly altered in patients with anorexia nervosa, bipolar disorder, and substance-related disorders. All reported studies support the consideration of cardiology consultations and a multidisciplinary approach in the care of patients with MDs with suspected cardiac dysfunction. Further investigation is warranted to determine the significance and prognostic value of myocardial deformation and strain measurements among individuals with MDs, focusing on the value of early detection, especially in asymptomatic cases. Full article

To manage and ensure the safety of traffic on rail networks, trains need to be reliably located at all times. This is achieved in many countries by electrically detecting their presence using so-called “track circuits” installed at regular intervals on each track, designed to detect when the wheels and axles of a train are shunting the two rails and to act accordingly on the signaling system. Such a detection principle is highly dependent on the quality of the electrical contacts between rails and wheels; the occurrence of high wheel–rail contact resistances can induce malfunctions known as “deshunting”, when the system is unable to judge the presence or absence of a train on a section of track. This type of potentially risky event must obviously be avoided at all costs. In this article, we focus on wheel–rail contact degradation resulting from steel oxidation, using a home-made, scaled-down test bench that reproduces real contact in the laboratory under controlled conditions. Given the complexity of the topic, the investigations are focused on static contact characterizations involving different degrees of rail oxidation and slow, stepwise variations in DC intensity. Full article

In this study, we present a novel approach using amperometric microsensors to detect quercetin in cosmetic formulations and track its metabolic behavior after topical application. This method offers a sensitive, real-time alternative to conventional techniques, enabling the detection of quercetin’s bioavailability, its transformation into active metabolites, and its potential therapeutic effects when applied to the skin. Quercetin (Q) is a bioactive flavonoid known for its potent antioxidant properties, naturally present in numerous plants, particularly those with applications in cosmetic formulations. In response to the growing interest in developing novel plant-based dermo-cosmetic solutions, this study investigates the electrochemical detection of quercetin, a ketone-type flavonoid, extracted from Gingko biloba essential oil. Three newly designed amperometric microsensors were developed to assess their efficacy in detecting quercetin in botanical samples. The sensor configurations utilized two forms of carbon material as a foundation: graphite (G) and carbon nanoparticles (CNs). These base materials were modified with paraffin oil, chitosan (CHIT), and cobalt(II) tetraphenylporphyrin (Co(II)TPP) to enhance sensitivity. Differential pulse voltammetry (DPV) served as the analytical method for this investigation. Among the sensors, the CHIT/G–CN microsensor exhibited the highest sensitivity, with a detection limit of 1.22 × 10⁻⁷ mol L⁻¹, followed by the G–CN (5.64 × 10⁻⁸ mol L⁻¹) and Co(II)TPP/G–CN (9.80 × 10⁻⁸ mol L⁻¹) microsensors. The minimum detectable concentration was observed with the G–CN and CoP/G–CN microsensors, achieving a threshold as low as 0.0001 μmol L⁻¹. Recovery rates and relative standard deviation (RSD) values averaged 97.4% ± 0.43, underscoring the sensors’ reliability for quercetin detection in botanical matrices. Full article