In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevat...
Detecting previously unseen malware remains a critical challenge for modern cybersecurity systems due to the rapid evolution of malicious software and the limitations of traditional supervised detection models. This paper proposes a Dual-Channel Mamb...
Telemetry data are the most important basis for ground operators to assess the status of satellites in orbit, and telemetry data-based anomaly detection has become a key tool to improve the reliability and safety of spacecrafts. Recent research on an...
The superposition principle in quantum mechanics enables the encoding of an entire solution space within a single quantum state. By employing quantum routines such as amplitude amplification or the Quantum Approximate Optimization Algorithm (QAOA), t...
Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of similar data points while ensuring dissimilar ones are far apart. The most recent studies focus on...
Sensor fusion has gained a great deal of attention in recent years. It is used as an application tool in many different fields, especially the semiconductor, automotive, and medical industries. However, this field of research, regardless of the field...
The rapid growth of artificial intelligence (AI) has enabled efficient crop disease detection even in data-scarce agricultural settings. This study proposes AgriFewNet, a few-shot learning framework designed to improve classification accuracy using R...
Automated Program Repair (APR) is increasingly used for vulnerability patching, yet many existing methods focus primarily on syntactic similarity between vulnerable and fixed code, with limited guarantees of semantic correctness and limited alignment...
In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a...
Accurate and early identification of breast cancer from mammography is key to reducing breast cancer mortality, and automated analysis is challenging due to subtle lesion appearances, heterogeneous breast density, and the variance caused by modality....
Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurrin...
Electroencephalography (EEG) is a cost-effective and noninvasive modality for identifying neurophysiological alterations associated with Alzheimer’s disease (AD). To robustly extract disease-specific features, this study presents contrastive mu...
To address the limitation of generalization of federated learning under non-independent and identically distributed (Non-IID) data, we propose FedDFPA, a personalized federated learning framework that integrates dynamic parameter fusion and prototype...
This paper addresses the fundamental problem of seed selection in semi-supervised clustering, where the quality of initial seeds has a significant impact on clustering performance and stability. Existing methods often rely on randomly or heuristicall...
Low-voltage AC series arc faults are a leading cause of electrical fires, posing significant risks to life and property. While artificial intelligence-based detection methods have achieved high accuracy, they often suffer from limited interpretabilit...
Interference classification plays an important role in anti-jamming communication. Although the existing interference signal recognition methods based on deep learning have a higher accuracy than traditional methods, these have poor robustness while...
Radio frequency (RF) fingerprinting, as an emerging physical layer security technology, demonstrates significant potential in the field of Internet of Things (IoT) security. However, most existing methods operate under a ‘closed-set’ assu...
Automatic modulation recognition (AMR) technology is a critical component of modern communication systems. However, conventional AMR methods based on the closed-set assumption struggle to detect unknown classes that may appear during testing. To addr...
The weakly supervised aircraft fine-grained detection task remains challenging due to the lack of fine-grained category labels. In addition, the high inter-class similarity among aircraft categories and complex background interference further hinders...
Industry 4.0 depends on the ability to connect heterogeneous systems, yet students and practitioners rarely have a low-risk environment in which to practice this kind of systems integration. This article presents a virtual-prototyping architecture de...
As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic si...
The quality of support sets plays a critical role in few-shot learning. How well class prototypes represent their corresponding categories depends directly on the samples selected to construct these sets during episodic training. In standard implemen...
The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course...
Advances in brain–computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical sy...
This study proposes a model for leafy vegetable disease detection and segmentation based on a few-shot learning framework and a prototype attention mechanism, with the aim of addressing the challenges of complex backgrounds and few-shot problems. Exp...
Finger vein recognition algorithms based on deep learning techniques are widely used in many fields. However, the training of finger vein recognition models is hindered by privacy issues and the scarcity of public datasets. Although applying federate...
Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be...
The misalignment between workforce competences and the requirements of digitally evolving occupations is a critical barrier to SME competitiveness. This study’s primary contribution is theoretical and methodological: it reconceptualizes the wor...
Time Series Classification (TSC) plays a crucial role in machine learning applications across domains such as healthcare, finance, and industrial systems. In these domains, TSC requires accurate predictions and reliable explanations, as misclassifica...
Deep learning-based surgical vision systems achieve strong performance in semantic segmentation and phase recognition, but their black-box nature limits traceability in safety-critical clinical settings. Prototype-based networks offer an interpretabl...
The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing coun...
Few-shot domain adaptation (FSDA) has become an important approach for cross-domain intrusion detection by enabling models to leverage limited labeled target data under distribution shifts. Although numerous adaptation methods have been proposed, the...
Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled dat...
To address the challenging problem of multi-scale inshore–offshore ship detection in synthetic aperture radar (SAR) remote sensing images, we propose a novel deep learning-based automatic ship detection method within the framework of compositio...
Accurate segmentation of retinal lesions is critical for the diagnosis and management of ophthalmic diseases, but pixel-level annotation is labor-intensive and demanding in clinical scenarios. To address this, we introduce a promptable segmentation a...
We focus on an estimation method based on deep learning in terms of fault correction time for the operation reliability assessment of open-source software (OSS) under the environment of an edge computing service. Then, we discuss fault severity level...
Due to the large amount of network traffic, manual network security management has become increasingly difficult. This paper proposes an open-source network anomaly detection system prototype that operates with the Security Onion 2.4 platform. The sy...
Speech-based depression detection provides a non-invasive and low-cost approach for supporting mental health screening. However, existing deep models often learn acoustic representations through fixed nonlinear transformations, which limits their abi...
Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the s...
Drug repositioning is a cost-effective approach to identifying new indications for existing drugs by predicting their associations with new diseases or symptoms. Recently, deep learning-based models have become the mainstream for drug repositioning....
Though deep learning has brought transformative advances to the field of modulation recognition, conventional approaches typically rely on a large amount of labeled data, which is often difficult to obtain in real-world communication scenarios. Few-s...
Deep learning has flourished in large-scale supervised tasks. However, in many practical conditions, rich and available labeled data are a luxury. Thus, few-shot learning (FSL) has recently received boosting interest and achieved significant progress...
In recent years, deep learning has been widely used in radar emitter signal identification and has significantly increased recognition rates. However, with the emergence of new institutional radars and an increasingly complex electromagnetic environm...
Data sparsity caused by limited interactions makes it challenging for recommendation to accurately capture user preferences. Contrastive learning effectively alleviates this issue by enriching embedding information through the learning of diverse con...
Continual learning (CL), also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-...
Prototype-Based Networks (PBNs) are inherently interpretable architectures that facilitate understanding of model outputs by analyzing the activation of specific neurons—referred to as prototypes—during the forward pass. The learned proto...
Landslides are among the most destructive natural hazards in mountainous and environmentally sensitive regions, causing significant damage to infrastructure, ecosystems, and human life. This study proposes a framework as a prototype Geo Digital Twin...
Image-based malware classification converts binary software into visual representations, enabling deep visual models to be used for malware family recognition. However, reliable family-level annotation remains labor-intensive and expert-dependent, ma...
Image classification interpretability remains a fundamental challenge in the field of computer vision. Despite the remarkable improvements achieved by deep neural networks in classification accuracy, their decision-making processes are often opaque,...
Rolling bearing fault diagnosis is essential for ensuring the safety and reliability of rotating machinery. Although deep learning-based methods have achieved promising performance, they usually require sufficient labeled data, which is difficult to...
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