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In order to realize efficient early warning of industrial product quality and safety risks, a risk assessment model based on the Bert model-improved sparrow search algorithm-least squares support vector machine (BERT-ISSA-LSSVM) algorithm is proposed. ...

The widespread use of machine learning in location-related scenarios is propelling the rapid development of intelligent transportation. To assist users in making more informed travel plans, the demand for improving prediction accuracy is growing. Prior to ...

Integrated opto-mechanical analysis (IOA) for Photoelectric detection and tracking systems (PDTS) is crucial both in the early design phase and during later operation and maintenance. The movements of PDTS typically generate unbalanced momentum, ...

In open-source software development, bug report templates (BRTs) have emerged as a crucial tool for ensuring the quality of bug reports. Despite their widespread use, developers have little knowledge about designing personalized BRTs. Therefore, it is ...

Text Sentiment Analysis (TSA) is a classic research topic in the field of Natural Language Processing (NLP) and has important application value for downstream tasks. Previous TSA methods focus on accurately representing emotional words in the ...

Online classification services based on machine learning have been widely used in fields such as healthcare and finance. To enhance the data privacy and avoid server collusion, companies usually deploy servers on different cloud service providers ...

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• Constant online communication rounds, efficient and robust classification service even under network delay.
• Secure redundant backup servers to enable fast system self-recovery without data re-upload.
• Client-friendly and lightweight ...

Vulnerability analysis is crucial for software security. Inspired by the success of pre-trained models on software engineering tasks, this work focuses on using pre-training techniques to enhance the understanding of vulnerable code and boost ...

Actor-critic deep reinforcement learning methods have demonstrated significant performance in many challenging decision-making and control tasks, but also suffer from high sample complexity and overestimation bias. Current researches focus on ...

Millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies are critical in current and future communication research. They play an essential role in meeting the demands for high-capacity, high-speed, and low-latency ...

As the core subject of IoT applications, IoT devices have faced numerous security challenges. Especially for IoT devices deployed in remote or harsh environments, they are often unattended for long periods, making it difficult to share the sensing data ...

The benefits of Particle Swarm Optimization (PSO) include its straightforwardness, simplicity, and powerful parallelism, so it is widely used to solve nonlinear, non-convex, high-dimensional problems. However, the typical particle swarm algorithm is ...

In this paper, we present Starrypia, a lightweight gamified music adjuvant treatment application to improve the symptoms of mild autistic children, eliminating the geographical and time constraints faced by traditional treatment. Adopting ABA (Applied ...

Event argument extraction (EAE) aims to recognize arguments that are entities involved in events and their roles. Previous prompt-based methods focus on designing appropriate prompt template for events, while neglecting to explicitly model ...

Vulnerability detection is essential to protect software systems. Various approaches based on deep learning have been proposed to learn the pattern of vulnerabilities and identify them. Although these approaches have shown vast potential in this task, ...

Currently, deep learning methods are being used to good effect in implementing finger vein (FV) recognition algorithms. However, increasingly large volumes of data can lead to problems in which FV recognition based on lightweight neural networks are ...

Recently, some Unbiased Gradient Sampling-based (UGS) methods have been proposed to enhance the security and efficiency of federated learning through crafted unbiased random transformation and sampling, such as MinMax Sampling in SIGMOD ’22. In ...

Event detection (ED) aims to recognize triggers and their types in sentences. Previous work employs distantly supervised methods or pre-trained language models to generate sentences containing events to alleviate data scarcity. Further, ...

Data imbalance, also known as the long-tail distribution of data, is an important challenge for data-driven models. In the Word Sense Disambiguation (WSD) task, the long-tail phenomenon of word sense distribution is more common, making it difficult to ...

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful ...

With the widespread application of unmanned aerial vehicles (UAVs), the discussion about the various security and privacy issues of UAVs have never stopped. Physical Unclonable Function (PUF) is inherently unclonable and tamper-proof, ...