Adversarial examples generated by perturbing raw data with carefully designed, imperceptible noise have emerged as a primary security threat to artificial intelligence systems. In particular, black-box adversarial attack algorithms, which only rely on model input and output to generate adversarial examples, are easy to implement in real scenarios. However, previous research on black-box attacks has

Convolutional neural networks (CNNs) are constrained in their capacity to model geometric transformations due to their fixed geometric structure. To overcome this problem, researchers introduce deformable convolution, which allows the convolution kernel to be deformable on the feature map. However, deformable convolution may introduce irrelevant contextual information during the learning process and

In this work, we consider the decentralized non-convex online optimization problem over an undirected network. To solve the problem over a communication-efficient manner, we propose a novel quantized decentralized adaptive momentum gradient descent algorithm based on the adaptive momentum estimation methods, where quantified information is exchanged between agents. The proposed algorithm not only can

The identification of vulnerabilities in smart contracts is necessary for ensuring their security. As a pre-trained language model, BERT has been employed in the detection of smart contract vulnerabilities, exhibiting high accuracy in tasks. However, it has certain limitations. Existing methods solely depend on features extracted from the final layer, thereby disregarding the potential contribution

This article focuses on a new application-oriented variant of vehicle routing problem. This problem comes from the daily distribution scenarios of a real-world logistics company. It is a large-scale (with customer sizes up to 2000), many-objective (with six objective functions) NP-hard problem with six constraints. Then, a local search with chain search path strategy (LS-CSP) is proposed to effectively

Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components

Reservoir oil-bearing recognition is the process of predicting reservoir types based on well logging data, which determines the accuracy of recognition. However, the original well logging data is multidimensional and contains potential noise, which can influence the performance of sequent processing, such as clustering and classification. It is crucial to obtain key low-dimensional features and study

Semantic segmentation of urban scenes is essential in urban traffic analysis and road condition information acquisition. The semantic segmentation model with good performance is the key to applying high-resolution urban locations. However, the types of these images are diverse, and the spatial relationships are complex. It is greatly affected by weather and light. Objects of different scales pose significant

General type-2 fuzzy logic systems (GT2 FLSs) based on the \(\alpha\)-planes representation of general T2 fuzzy sets (FSs) have become more accessible to FL investigators in recent years. Type reduction (TR) is the most important block for GT2 FLSs. Here the weighted type-reduction algorithms based on the Newton and Cotes quadrature formulas of numerical methods of integration technique are first given

Time series forecasting plays a crucial role across various sectors, including energy, transportation, meteorology, and epidemiology. However, existing models often struggle with capturing long-term dependencies and managing computational efficiency when handling complex and extensive time series data. To address these challenges, this paper introduces the BSAformer model, which leverages a unique

Remote sensing image pairs of different time phases have complex and changeable semantic contents, and traditional convolutional registration methods are challenging in modeling subtle local changes and global large-scale deformation differences in detail. This results in poor registration performance and poor feature representation. To address these problems, a semantically enhanced dual-graph perception

Feature selection is a common method for improving classification performance. Selecting features for high-dimensional data is challenging due to the large search space. Traditional feature ranking methods that search for top-ranked features cannot remove redundant and irrelevant features and may also ignore interrelated features. Evolutionary computation (EC) techniques are widely used in feature

Sample augmentation, especially sample generation is conducive for addressing the challenge of training robust image and video object detection models based on the deep learning. Still, the existing methods lack sample editing capability and suffer from annotation work. This paper proposes an image sample generation method based on key box points detection and Generative adversarial network (GAN),

In this paper, we present a novel localization algorithm for mobile robots navigating in complex planar environments, a critical capability for various real-world applications such as autonomous driving, robotic assistance, and industrial automation. Although traditional methods such as particle filters and extended Kalman filters have been widely used, there is still room for assessing the capabilities

Evolutionary multi-objective algorithms have been applied to beam angle optimization (called BAO) for generating diverse trade-off radiotherapy treatment plans. However, their performance is not so effective due to the ignorance of using the specific clinical knowledge that can be obtain intuitively by clinical physicist. To address this issue, we suggest a pattern mining based evolutionary multi-objective

Although skeleton-based gesture recognition using supervised learning has achieved promising results, the reliance on extensive annotated data poses significant costs. This paper addresses the challenge of semi-supervised skeleton-based gesture recognition, to effectively learn feature representations from labeled and unlabeled data. To resolve this problem, we propose a novel multimodal multilevel

Multi-objective evolutionary optimization is widely utilized in industrial design. Despite the success of multi-objective evolutionary optimization algorithms in addressing complex optimization problems, research focusing on input disturbances remains limited. In many manufacturing processes, design parameters are vulnerable to random input disturbances, resulting in products that often perform less

The booming development of the Internet of Things (IoT) has led to an explosion of web services, making it more inconvenient for users to choose satisfactory services among numerous options. Therefore, ensuring quality of service (QoS) in a service-oriented IoT environment is crucial, highlighting QoS prediction as a prominent research focus. However, issues related to information credibility, user

In order to reduce the burden of DBA, the knob tuning method based on reinforcement learning has been proposed and achieved good results in some cases. However, the performance of these solutions is not ideal as the workload features are not considered enough. To address these issues, we propose a database tuning system called ADWTune. In this model, ADWTune employs the idea of multiple sampling to

With the rapid growth in both the variety and volume of data on networks, especially within social networks containing vast multimedia data such as text, images, and video, there is an urgent need for efficient methods to retrieve helpful information quickly. Due to their high computational efficiency and low storage costs, unsupervised deep cross-modal hashing methods have become the primary method

This paper introduces DOAMRL, a novel meta-reinforcement learning (meta-RL) method that extends the Model-Agnostic Meta-Learning (MAML) framework. The method addresses a key limitation of existing meta-RL approaches, which struggle to effectively use suboptimal demonstrations to guide training in sparse reward environments. DOAMRL effectively combines reinforcement learning (RL) and imitation learning

Driver action recognition is crucial for in-vehicle safety. We argue that the following factors limit the related research. First, spatial constraints and obstructions in the vehicle restrict the range of motion, resulting in similar action patterns and difficulty collecting the full body posture. Second, in skeleton-based action recognition, establishing the joint dependencies by the self-attention

Multimodal sentiment analysis (MSA) has garnered significant attention for its immense potential in human-computer interaction. While cross-modality attention mechanisms are widely used in MSA to capture inter-modality interactions, existing methods are limited to pairwise interactions between two modalities. Additionally, these methods can not utilize the causal relationship to guide attention learning

The rise of social media has led to an increase in hate speech. Hate speech is generally described as a deliberate act of aggression aimed at a particular group, intended to harm or marginalize them based on specific attributes of their identity. While positive interactions in diverse communities can greatly enhance confidence, it is important to acknowledge that negative remarks such as hate speech

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by difficulties in social skills, repetitive behaviours, and communication. Early and accurate diagnosis is essential for effective intervention and support. This paper proposes a secure and privacy-preserving framework for diagnosing ASD by integrating multimodal kinematic and eye movement sensory data, Deep Neural Networks

Trajectory prediction has become increasingly critical in various applications such as autonomous driving and robotic navigation. However, due to the significant variations in trajectory patterns across different scenarios, models trained in known environments often falter in unseen ones. To learn a generalized model that can directly handle unseen domains without requiring any model updating, we propose

Multi-task learning is important in reinforcement learning where simultaneously training across different tasks allows for leveraging shared information among them, typically leading to better performance than single-task learning. While joint training of multiple tasks permits parameter sharing between tasks, the optimization challenge becomes crucial—identifying which parameters should be reused

Class imbalance is a prevalent issue in practical applications, which poses significant challenges for classifiers. The large margin distribution machine (LDM) introduces the margin distribution of samples to replace the traditional minimum margin, resulting in extensively enhanced classification performance. However, the hyperplane of LDM tends to be skewed toward the minority class, due to the optimization

Chaos theory, with its unique blend of randomness and ergodicity, has become a powerful tool for enhancing metaheuristic algorithms. In recent years, there has been a growing number of chaos-enhanced metaheuristic algorithms (CMAs), accompanied by a notable scarcity of studies that analyze and organize this field. To respond to this challenge, this paper comprehensively analyzes recent advances in

The rise of Large Language Models (LLMs) has further led to the development of text summarization techniques and also brought more attention to the problem of hallucination in the research of text summarization. Existing work in current text summarization research based on LLMs typically uses In-Context Learning (ICL) to supply accurate (document-summary) pairs of samples to the model, thus allowing

Single Image Defocus Deblurring (SIDD) remains challenging due to spatially varying blur kernels, particularly in processing high-resolution images where traditional methods often struggle with artifact generation, detail preservation, and computational efficiency. This paper presents Swin-Diff, a novel architecture integrating diffusion models with Transformer-based networks for robust defocus deblurring

Most of existing non-invasive load monitoring (NILM) methods usually ignore the complementarity between temporal and spatial characteristics of appliance power data. To tackle this problem, this paper proposes a spatio-temporal attention fusion network with a sequence-to-point learning scheme for load disaggregation. Initially, a temporal feature extraction module is designed to extract temporal features

With the development of data acquisition technology, a large amount of time-series data can be collected. However, handling too much data often leads to a waste of social resources. It becomes significant to determine the minimum data size required for training. In this paper, a framework for neural ordinary differential equations based on incremental learning is discussed, which can enhance learning

Micro-expressions (MEs) are unconscious and involuntary reactions that genuinely reflect an individual’s inner emotional state, making them valuable in the fields of emotion analysis and behavior recognition. MEs are characterized by subtle changes within specific facial action units, and effective feature learning and fusion tailored to these characteristics still require in-depth research. To address

Video action recognition is a fundamental task in video understanding. Actions in videos may vary at different speeds or scales, and it is difficult to cope with a wide variety of actions by relying on a single spatio-temporal scale to extract features. To address this problem, we propose a Motion-Aware Network (MANet), which includes three key modules: (1) Local Motion Encoding Module (LMEM) for capturing

Improved scheduling of underground transportation vehicles in coal mines can significantly enhance work efficiency and contribute to safer production. However, the specific working conditions and limitations of electric vehicles pose significant challenges to effective vehicle scheduling. To address this issue, a constrained single-objective optimization model is developed to minimize transportation

Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. We extended the idea of artificial neural networks to continuous linear Diophantine fuzzy neural networks. A few operational concepts for continuous linear Diophantine fuzzy sets are further developed, and they are subsequently made simpler to apply to more than two such sets

Real-time, accurate, and robust positioning system plays a crucial role in many vehicular applications for automatic driving system and Vehicular Ad-hoc Network (VANET). In the tunnel, the positioning accuracy of Global Navigation Satellite System (GNSS) decreases due to blocked satellite signals. In order to estimate the exact location of vehicles in tunnel environments, many positioning systems have

Federated learning is a distributed machine learning paradigm that emerged as a solution to the need for privacy protection in artificial intelligence. Like traditional machine learning, federated learning is threatened by multiple attacks, such as backdoor attacks, Byzantine attacks, and adversarial attacks. The weaknesses are exacerbated by the inaccessibility of data in federated learning, which

Single image super resolution has significantly advanced by utilizing transformers-based deep learning algorithms. However, challenges still need to be addressed in handling grid-like image patches with higher computational demands and addressing issues like over-smoothing in visual patches. This paper presents a Deep Learning model for single-image super-resolution. In this paper, we present the XTNSR

When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. In this paper, a novel hierarchical decoding network for image inpainting is proposed. Firstly, the structural priors extracted from the encoding layer are utilized to guide the first decoding layer, while residual

Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic

Instant delivery service has brought great convenience to our modern life. In order to improve its efficiency, multi-depot pick-up-and-delivery location routing problem with time windows (MDPDLRPTW) is proposed in this paper. Existing works related to MDPDLRPTW focus on obtaining a depot location scheme by clustering and perform route planning on it through single-task optimization. They are powerless

This article presents a new crossover operator for problems with an underlying graph structure where edges point to prospective interdependence relationships between decision variables and neighborhoods shape the definition of the global objective function via a sum of different expressions, one for each neighborhood. The main goal of this work is to propose a crossover operator that is broadly applicable

In the low-carbon economy, the fresh industry constitutes an “impossible triangle” in products, prices and services. Therefore, based on the idea of cost–benefit, a comprehensive vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer is presented. Then, a hybrid algorithm called local search genetic algorithm (LSGA) is proposed, which

Developing a robust algorithm for underwater object tracking (UOT) is crucial to support the sustainable development and utilization of marine resources. In addition to open-air tracking challenges, the visual object tracking (VOT) task presents further difficulties in underwater environments due to visual distortions, color cast issues, and low-visibility conditions. To address these challenges, this

Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transferable adversarial attacks exhibit a notably high stealthiness and detection difficulty, making them a significant

Achieving accurate and generalized autonomous navigation in unknown environments poses a significant challenge in robotics and artificial intelligence. Animals exhibits superlative navigation capabilities by combining the representation of internal neurals and sensory cues of self-motion and external information. This paper proposes a brain-inspired navigation method based upon the spiking neural networks

In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MBR). Existing MBR task faces two primary challenges. First, the irrelevant auxiliary behaviors that do not align

Forecasting the remaining useful life (RUL) is a crucial aspect of prognostics and health management (PHM), which has garnered significant attention in academic and industrial domains in recent decades. The accurate prediction of RUL relies on the creation of an appropriate degradation model for the system. In this paper, a general representation of diffusion process models with three sources of uncertainty

This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings

Recently, scientists have widely utilized Artificial Intelligence (AI) approaches in intelligent agriculture to increase the productivity of the agriculture sector and overcome a wide range of problems. Detection and classification of plant diseases is a challenging problem due to the vast numbers of plants worldwide and the numerous diseases that negatively affect the production of different crops

Automated test case generation for path coverage (ATCG-PC) is a major challenge in search-based software engineering due to its complexity as a large-scale black-box optimization problem. However, existing search-based approaches often fail to achieve high path coverage in large-scale unit programs. This is due to their expansive decision space and the presence of hundreds of feasible paths. In this

The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a hot topic of interest in both industry and academia. To address this, this paper proposes a Preference-Based Mask-PPO (PBMP) algorithm, which leverages

Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. This paper proposed

This paper proposes a surrogate-assisted dynamic population optimization algorithm (SDPOA) for the purpose of solving computationally expensive constrained optimization problems, in which the population is dynamically updated based on the real-time iteration information to achieve targeted searches for solutions with different qualities. Specifically, the population is dynamically constructed by simultaneously

Numerous surrogate-assisted evolutionary algorithms are developed for multi-objective expensive problems with low dimensions, but scarce works have paid attention to that with high dimensions, i.e., generally more than 30 decision variables. In this paper, we propose a multi-mode radial basis functions-assisted evolutionary algorithm (MMRAEA) for solving high-dimensional expensive multi-objective optimization

This paper is concerned with the zonotopic state estimation problem for a class of linear repetitive processes (LRPs) with weighted try-once-discard protocols (WTODPs) subject to uniform quantization. In such a system, the process disturbance and measurement noise are generally assumed to be unknown but bounded in certain zonotopes. The measurement data are uniformly quantized prior to entering the

With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete

Although deep reinforcement learning has achieved notable practical achievements, its theoretical foundations have been scarcely explored until recent times. Nonetheless, the rate of convergence for current neural temporal-difference (TD) learning algorithms is constrained, largely due to their high sensitivity to stepsize choices. In order to mitigate this issue, we propose an adaptive neural TD algorithm