We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning probl...
Detection of AI-generated content is a crucially important task considering the increasing attention towards AI tools, such as ChatGPT, and the raised concerns with regard to academic integrity. Existing text classification approaches, including neur...
Time-series anomaly detection is imperative for ensuring reliability and safety in intelligent manufacturing systems. However, real-world environments typically provide only normal operating data and exhibit significant periodicity, noise, imbalance,...
Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classificati...
The analysis of fast fluorescence kinetics, specifically through the JIP test, is a valuable tool for identifying and characterizing plant stress. However, interpreting OJIP data requires a comprehensive understanding of their underlying theory. This...
In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be a...
Network intrusion detection technology is key to cybersecurity regarding the Internet of Things (IoT). The traditional intrusion detection system targeting Binary or Multi-Classification can detect known attacks, but it is difficult to resist unknown...
In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional los...
Recently, the dangers associated with face generation technology have been attracting much attention in image processing and forensic science. The current face anti-spoofing methods based on Generative Adversarial Networks (GANs) suffer from defects...
Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unm...
Intrusion detection systems are crucial for cybersecurity applications. Network traffic data originate from diverse terminal sources, exhibiting multi-view feature spaces, while the collection of unknown intrusion data is costly. Current one-class cl...
Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying...
Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly....
Machine learning and artificial intelligence models have become popular for climate change prediction. Forested regions in California and Western Australia are increasingly facing intense wildfires, while other parts of the world face various climate...
In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth, which has inspired the search for more effective operations. Nevertheless, different PV faults may appear, which leads to various degradation stages. Furthermore,...
The network system has become an indispensable component of modern infrastructure. DDoS attacks and their variants remain a potential and persistent cybersecurity threat. DDoS attacks block services to legitimate users by incorporating large amounts...
Electricity theft, emerging as one of the severe cyberattacks in smart grids, causes significant economic losses. Due to the powerful expressive ability of deep neural networks (DNN), supervised and unsupervised DNN-based electricity theft detection...
Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this...
This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to...
Automated visual inspection has become a critical component of Industry 4.0 manufacturing systems, enabling improved product quality, reduced operational costs, and increased production efficiency. In the carpet manufacturing industry, quality inspec...
10 December 2024
Detecting malware has become extremely important with the increasing exposure of computational systems and mobile devices to online services. However, the rapidly evolving nature of malicious software makes this task particularly challenging. Despite...
Voice phishing, commonly known as vishing, has become one of the fastest-growing threats in social engineering. The rapid advancement and accessibility of AI voice cloning tools have enabled attackers to produce highly convincing synthetic speech at...
Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D)...
In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operate...
In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movemen...
Reliable bearing fault detection is essential for predictive maintenance in industrial systems; however, obtaining labelled fault data is often expensive, time-consuming, and impractical in real-world deployments. To address this challenge, this stud...
Deep learning has increasingly been used to identify minerals. However, deep learning can only be used to identify minerals within the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categori...
Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are...
Automated Visual Examination (AVE) is a deep learning algorithm that aims to improve the effectiveness of cervical precancer screening, particularly in low- and medium-resource regions. It was trained on data from a large longitudinal study conducted...
As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assurin...
At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special...
Conveyors are used commonly in industrial production lines and automated sorting systems. Many applications require fast, reliable, and dynamic detection and recognition for the objects on conveyors. Aiming at this goal, we design a framework that in...
Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communi...
The electric vehicles (EVs) market keeps growing globally; thus, it is critical to secure the EV charging communication protocols in order to guarantee reliable and fair charging operations among the customers. The Open Charge Point Protocol (OCPP) 2...
Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven struct...
Traditional wood identification models are vulnerable to out-of-distribution (OOD) substitution in the global timber trade. In response to this issue, this study presents a dual-stage cascade authentication architecture using in situ Raman spectrosco...
The increasing frequency of mass shootings at public events and public buildings underscores the limitations of traditional surveillance systems, which rely on human operators monitoring multiple screens. Delayed response times often hinder security...
In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning...
The security of programmable logic controllers (PLCs) that control industrial systems is becoming increasingly critical due to the ubiquity of the Internet of Things technologies and increasingly nefarious cyber-attack activity. Conventional techniqu...
Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work,...
Prompt injection, ranked first in the OWASP Top 10 for Large Language Model (LLM) applications, enables adversaries to override system instructions and exfiltrate sensitive information by crafting inputs that blur the boundary between data and contro...
NAND flash-based solid state drives (SSDs) are increasingly common in computers, but they present a problem for forensic data recovery. SSDs use controller logic, flash translation layers, error correction, wear leveling, TRIM, garbage collection, an...
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightenin...
Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is...
In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaini...
In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously int...
This research advances the study of learning under non-stationary conditions by proposing a unified framework for concept drift detection and adaptive regression in evolving data streams. Unlike traditional batch models that assume static data distri...
The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific tra...
Reconstruction-based approaches to anomaly detection tend to fall short when applied to complex datasets with target classes that possess high inter-class variance. Similar to the idea of self-taught learning used in transfer learning, many domains a...
The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the gene...
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