Object detection algorithm in urban traffic using remote sensing images often suffers from high complexity, low real-time performance, and low accuracy. To address these challenges, we propose RTL-Net, an urban traffic object detection network structure based on You Only Look Once (YOLO) v8s. To enhance real-time performance beyond the benchmark, we implemented lightweight designs for the loss function

DETR (DEtection TRansformer) is a CV model for object detection that replaces traditional complex methods with a Transformer architecture, and has achieved significant improvement over previous methods, particularly in handling small and medium-sized objects. However, the attention mechanism-based detection framework of DETR exhibits limitations in small and medium-sized object detection. It struggles

With the rapid development of the Internet, the existence of fake news and its rapid spread has brought many negative effects to the society. Consequently, the fake news detection task has become increasingly important over the past few years. Existing methods are predominantly unimodal methods or the multimodal representation of unimodal fusion for fake news detection. However, the large number of

Multiple vessel tracking plays a vital role in maritime surveillance systems. Previous studies have typically integrated object detection and trajectory association techniques to address this problem, but they still face some significant challenges. On one hand, these methods are susceptible to losing tracked targets due to long-term occlusion by other obstacles or slow-moving vessels in inland waterways

To address the issues of low discovery probability, inefficient search, and antagonistic targets during the process of dynamic target search in the ocean, a dynamic target search path planning method based on the Glasius biologically-inspired neural network (GBNN) in combination with marine environmental information is proposed. Firstly, the motion model of the searcher and the capability model of

This paper introduces a real-time head-pose detection and eye-gaze estimation system for Automatic Driver Assistance Technology (ADAT) aimed at enhancing driver safety by accurately collecting and transmitting data on the driver’s head position and eye gaze to mitigate potential risks. Existing methods are constrained by significant limitations, including reduced accuracy under challenging conditions

Network robustness can be effectively augmented through edge safeguarding, especially when topology modification is not feasible. Although approximation algorithms are used due to the intrinsic hardness of problem, when the connectivity of the initial graph is adjusted to the desired value, the connectivity of the concealed weak graph is escalated to a maximum level. Consequently, a substantial amount

Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. However, due to the scarcity of training samples, the prediction accuracy of frequently-used regression surrogate models can hardly be guaranteed as the difficulty of the problem increases, resulting in performance degradation of the whole algorithm. Since real-world

Knowledge tracing (KT) aims to predict students’ future performance by assessing their level of knowledge mastery from past problem-solving records. However, many existing methods fail to take full advantage of the potential relationship between questions and skills, or fail to effectively utilize students’ historical learning data, which makes it difficult to accurately capture individualized mastery

Remanufacturing has become a mainstream sustainable manufacturing paradigm for energy conservation and environmental protection. Disassembly and reprocessing operations are two main activities in remanufacturing. This work proposes multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and random processing time. First, a stochastic programming

n-intuitionistic polygonal fuzzy sets have significant advantages over traditional fuzzy sets in handling uncertain information. Due to the fact that information measure is an effective tool for handling uncertain information, this paper proposes distance measures, symmetric cross entropies and knowledge measures for n-intuitionistic polygonal fuzzy sets. First, this paper initially formulates distance

This paper proposes a multi-granularity feature intersection network (MGFINet) for visible-infrared person re-identification (VI-ReID). VI-ReID aims to retrieve images of the same pedestrian from different spectral cameras. The key challenge is to extract pedestrian descriptions with both inter-class discriminability and intra-class similarity. Previous methods ignore the potential loss of details

The multi-UAV relay system can rapidly deploy a temporary communication network in disaster emergency communication scenarios to enhance communication coverage and stability in the affected area and ensure the efficient transmission of rescue information. Aiming at the problems of insufficient real-time performance, low user fairness, and low utilization of communication resources in multi-UAV relay

Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. Integrating Privacy Information Retrieval (PIR) mechanisms within business models is crucial to address privacy concerns, ensure compliance with regulations, safeguard sensitive data, and maintain trust with stakeholders; however, PIR is not included in the existing business models

In real-world network environments, advanced persistent threats (APTs) are characterized by their complexity and persistence. Existing APT detection methods often struggle to comprehensively capture the complex and dynamic network relationships and covert attack patterns involved in the attack process, and they also suffer from insufficient detection effectiveness. To address this, we propose a model

Multi-view multi-label classification (MvMLC) has garnered significant interest because of its ability to handle complex datasets. However, the inherent complexity of real-world data often results in incomplete views and missing labels, which limit the richness of data and hinder the accurate association of features with their corresponding categories. Additionally, the MvMLC task is intricate due

This paper introduces a new approach to predict human engagement in human–robot interactions (HRI), focusing on eye contact and distance information. Recognising engagement, particularly its decline, is essential for successful and natural interactions. This requires early, real-time user behavior detection. Previous HRI engagement classification approaches use various audiovisual features or adopt

Emotion recognition in conversations has recently emerged as a hot research topic owing to its increasingly important role in developing intelligent empathy services. Thoroughly exploring the conversational context and accurately capturing emotion-shift information are highly crucial for accurate emotion recognition in conversations. However, existing studies generally failed to fully understand the

Graph neural networks (GNNs) have exhibited remarkable performance in addressing diverse graph learning tasks. However, inevitable missing information in graph networks hinders GNNs from aggregating more abundant feature information, limiting GNNs’ performance. Moreover, missing information further exacerbates the risk of overfitting in GNNs. In this manuscript, we devote to presenting a novel framework

In recent years, text-to-image generation technology based on diffusion models has made significant progress, but extending it to the field of video generation, especially under few-shot conditions, still faces huge challenges. Existing methods usually rely on a large amount of text-video pair data or consume a lot of training resources. Based on this, this paper proposes a new few-shot video generation

Referring video object segmentation (RVOS) aims to segment the object corresponding to a language expression in a video. Most existing RVOS methods are trained using accurate per-pixel annotations, which are expensive and time-consuming to obtain. Moreover, they need to update the entire parameter of a segmentation model, making it inefficient to train as the model scale increases. In this paper, we

In this work we analyze the problem of, given the probability distribution of a population, questioning an unknown individual that is representative of the distribution so that our uncertainty about certain characteristics is significantly reduced—but the uncertainty about others, deemed private or sensitive, is not. Thus, the goal of the problem is extracting information being relevant to a legitimate

The variable flow ducted rocket (VFDR) poses significant challenges for high-precision modeling due to its complex nonlinear dynamics, harsh operational conditions, and integration of multiple physical fields. To address this challenge, this paper introduces a hybrid mechanism and data-driven modeling approach. Initially, the parameter perturbation method was employed to elucidate the interdependencies

In this article, we introduce the technique of circular Pythagorean fuzzy soft (CPFS) sets and their reliable properties, such as algebraic optional laws and Einstein operational laws. We further develop the CPFS Einstein weighted averaging (CPFSEWA) operator and the CPFS Einstein weighted geometric (CPFSEWG) operator, highlighting their fundamental properties. Additionally, we integrate the evaluation

In complicated environments, which include dynamic and narrow areas, the path planning of Autonomous Mobile Robots (AMRs) encounters challenges, like slow model convergence and limited representational capabilities, often resulting in the robot taking longer, less efficient paths or even colliding with obstacles. To tackle these challenges, the Gated Attention Prioritized Experience Replay Soft Actor-Critic

Traditional language models have been extensively evaluated for software engineering domain, however the potential of ChatGPT and Gemini have not been fully explored. To fulfill this gap, the paper in hand presents a comprehensive case study to investigate the potential of both language models for development of diverse types of requirement engineering applications. It deeply explores impact of varying

Vision-based environmental perception has demonstrated significant promise for autonomous driving applications. However, the traditional unidirectional feature flow in many perception networks often leads to inadequate information propagation, which hinders the system’s ability to comprehensively perceive complex driving environments. Issues such as similar objects, illumination variations, and scale

Object detection from the perspective of unmanned aerial vehicles (UAV) is a technology that utilizes visual sensors mounted on UAV to automatically identify and locate ground targets. However, due to the small size of targets captured by UAV, along with challenges such as scale variation and blurred edges, existing methods struggle to maintain high detection accuracy while ensuring efficient inference

The early detection of lung diseases, including cancer, is essential for improving patient outcomes. However, traditional diagnostic approaches and standard deep learning models often face challenges in effectively analyzing the complex spatial and temporal variations in medical imaging data, particularly in CT scans. To address these challenges, we propose DC-CAD, a novel hybrid framework that integrates

In industrial cases, robustness of the robots is mandatory and thus the development of fault diagnosis systems is essential. This study introduces a novel fault diagnosis method that merges two elements: Two methods shared here are the hierarchical hyper-Laplacian prior (HHLP) and singular spectrum analysis (SSA). The SSA technique decomposes the encoder signals into three components; residual, periodic

Detecting money laundering within financial networks presents a complex challenge due to the elusive behavior patterns of laundering agents, often resulting in data gaps. In this research, we propose a ‘Multiverse Simulation’ framework using a multi-agent system to generate synthetic datasets for anti-money laundering (AML) training and detection. This framework creates diverse virtual worlds, each

Task-oriented grasping (TOG) aims to predict the appropriate pose for grasping based on a specific task. While recent approaches have incorporated semantic knowledge into TOG models to enable robots to understand linguistic commands, they lack the ability to leverage relevant information from vision, language, and action. To address this problem, we propose a novel multimodal fusion grasping framework

This paper presents a study on the multi-objective green four-dimensional transportation problem (MOG4DTP) with product blending. Due to uncontrollable circumstances and globalization, it is not always practical to exactly determine the parameters of the MO4DGTP. In such situations, decision experts sometimes have to deal with data that can be described by a membership degree (MD) and a non-membership

Prioritized Experience Replay enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. However, this non-uniform sampling method shifts the state-action distribution that is originally used to estimate Q-value functions, which brings about the estimation deviation. In this article, a novel off-policy reinforcement learning training framework

Deep learning (DL) applications have potential for improving the accuracy of type II diabetes diagnoses. However, existing DL applications for the diagnosis of type II diabetes have several drawbacks. For example, they maximize overall diagnostic performance rather than the diagnostic performance for each patient, they do not use objective rules to identify whether a patient has type II diabetes, and

Image restoration is a challenging and complex problem involving recovering the original clear image from a degraded or noisy image. In the medical field, image restoration techniques can significantly improve the quality of endoscopic images, helping doctors make more accurate diagnoses and providing higher-quality data support for computer vision-assisted detection. Existing methods for image restoration

Recently, the prosperity of linear models has raised questions about capturing the sequential capabilities of Transformer forecasters. Although the latest Transformer-based studies have alleviated some of these concerns, the limited information utilization still constrains the model’s comprehensive exploration of complex dependencies, as these forecasters often prioritize global dependence on time

The vectorized representation of a knowledge graph is essential for effectively utilizing its implicit knowledge. Graph neural networks (GNNs) are particularly adept at learning graph representations due to their ability to handle graph topologies. However, GNN-based approaches face two main challenges: first, they fail to differentiate between the types of adjacent nodes during the information aggregation

Surface quality is critical for the performance of high-end equipment, with defects potentially leading to severe operational failures. Current defect detection methods face challenges: 2D imaging lacks the ability to capture scratch depth, limiting quantitative damage assessment, while 3D point cloud methods are costly and time-consuming, hindering scalability in manufacturing. This study proposes

Graph data-driven machine learning methods for human activity recognition (HAR) have achieved success recently using sufficient data. In the realm of everyday life, we encounter a notable challenge: the scarcity of labeled radar samples. This limitation is compounded by the stark disparities in data distribution between simulated and measured activity domains. In this article, a generalized graph contrastive

The accurate determination of results in decision analysis is usually predicated on the association between two factors. Although generating data for analytical purposes presents an apparent hurdle, the data obtained may present hurdles in its interpretation. Correlation coefficients can be used to analyze the interaction between two factors and their variations. These coefficients deliver an objective

Ischemic stroke, a leading cause of death and disability, necessitates accurate detection and automatic segmentation of lesions. While diffusion weight imaging is crucial, its single modality limits the detection of subtle lesions and artifacts. To address this, we propose a multi-modality, multi-level fusion network (M2FNet) that aggregates salient features from different modalities across various

Homomorphic encryption is a recent and fundamental breakthrough in modern cryptography, which allows the performance of operations on encrypted data without unveiling the data. Leveraging quantum mechanics principles, quantum computers can potentially solve certain computational problems exponentially faster than classical computers. This immense computational power offers new possibilities for various

Cross-domain sequential recommendation (CDSR) aims to predict future sequential interactions in a target domain by analyzing historical sequence data from different domains. A significant challenge in CDSR is the accurate capture of user preferences based on the target domain and multiple domains. Existing methodologies to enhance the performance of the target domain primarily focus on learning preferences

Predicting the Remaining Useful Life (RUL) of industrial equipment is essential for proactive maintenance and health assessment, particularly under the computational constraints of edge devices. While deep learning methods, such as Long Short-Term Memory (LSTM) networks, excel at modeling complex time series, their high computational cost often restricts real-time deployment. To address this challenge

Unsupervised feature selection (UFS) methods play a crucial role in improving the efficiency of extracting relevant information and reducing computational complexity in the context of big data analysis. Despite notable advancements in the field of unsupervised feature selection for large-scale datasets, many UFS methods still remain redundant and irrelevant features during the feature selection process

Traditional recommendation systems tend to focus on accuracy and prefer recommending popular items, resulting in non-popular items rarely being exposed to users. However, recommending non-popular items to enhance users’ novelty experience is also crucial. Currently, many researchers are dedicated to multi-objective recommendation studies. Nevertheless, existing multi-objective recommendation algorithms

Unsupervised multi-source domain adaptation methods transfer knowledge learned from multiple labeled source domains to an unlabeled target domain. Existing methods assume that all source domain data can be accessed directly. However, such an assumption is unrealistic and causes data privacy concerns, especially when the source domain labels include personal information. In such a setting, it is prohibited

Decentralized federated learning (DFL) represents a distributed learning framework where participating nodes independently train local models and exchange model updates with proximate peers, circumventing the reliance on a centralized orchestrator. This paradigm effectively mitigates server-induced bottlenecks and eliminates single points of failure, which are inherent limitations of centralized federated

The impact of airborne pollutants present in the environment, entering the body through breathing, can cause significant risks of respiratory and heart-related health problems for individuals. For this, different air purifiers are commonly used to eliminate delicate particulate matter PM2.5, and various studies have examined their effectiveness. This paper aims to analyze airborne pollutants and, by

Label correlations, as important prior information, are essential to enhance the classification performance in Multi-Instance Multi-Label (MIML) algorithms, but existing models always leverage global label correlations which are less informative. Furthermore, classifier optimization is also crucial for MIML classification results, previous works do not frequently seek to optimize multi objectives simultaneously

The management of complex, dynamic, and cross-domain resources in cyber-physical-human systems (CPHS) faces significant challenges under spatiotemporal dynamics, particularly resource state conflicts caused by rapid environmental changes and interdependent resource interactions. To address these challenges, this study proposes an integrated framework combining resource modeling and resource state adaptive

The large action space is a key challenge in reinforcement learning. Although hierarchical methods have been proven to be effective in addressing this issue, they are not fully explored. This paper combines domain knowledge with hierarchical concepts to propose a novel Hierarchical Reinforcement Learning framework based on macro actions (HRL-MA). This framework includes a macro action mapping model

Fleet formation identification in remote sensing is a significant focus in maritime surveillance. However, fleet may occur with different ship dense and noisy data due to the complex background and different satellite resolution, few studies have discussed formation identification considering the limits of sensing and application. This study introduces an effective fleet formation identification and

Cross-resolution Person Re-identification (ReID) faces the significant challenge of large resolution variance across different camera views in real surveillance systems. Most approaches based on super-resolution (SR) excessively rely on the SR images, which may lead to the loss of low-resolution (LR) information. Meanwhile, the region-agnostic SR could pose interference to ReID. For this, we propose

Open Set Domain Adaptation (OSDA) aims to minimize domain variation while distinguishing between known and unknown samples. However, existing OSDA methods, which rely on deep neural network classifiers, often lead to overconfident predictions and fail to clearly demarcate known from unknown samples. To address this limitation, we propose the Energy-based Open Set domain adaptation (EOS) method. EOS

Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel multi-stage semi-adaptive DE algorithm with enhanced diversity (MSA-DE), offering several key contributions: first

Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. In recent years, multi-behavior recommendation has addressed the challenges of data sparsity and cold start problems to some extent. However, the introduction of noise from multi-behavior tasks into the user-item graph exacerbates the impact of noise from a few active

Accurate identification of tree species is essential for effective forestry management and conservation. Simple deep-learning models, such as AlexNet and VGG16, often struggle with fine-grained texture extraction and feature distinction, especially in complex environments. While more advanced models, such as ResNet34 and deeper architectures, offer superior feature extraction capabilities, they come

Effective identification of interdependence information between decision variables is crucial for variable grouping in large-scale global optimization (LSGO). This paper introduces a novel approach called FCA-G (Formal Concept Analysis-Driven Grouping) to solve LSGO problems. FCA, an effective tool for data analysis, is employed in this approach. The primary contribution involves transforming decision