High-Precision Pest Management Based on Multimodal Fusion and Attention-Guided Lightweight Networks.

In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly rely on single-modal inputs and suffer from poor recognition stability under complex field conditions, a multimodal recognition framework has been proposed. This framework integrates RGB imagery, thermal infrared imaging, and environmental sensor data.

Efficient AES Side-Channel Attacks Based on Residual Mamba Enhanced CNN.

With the continuous advancement of side-channel attacks (SCA), deep learning-based methods have emerged as a prominent research focus due to their powerful feature extraction and nonlinear modeling capabilities. Traditional convolutional neural networks (CNNs) excel at capturing local temporal dependencies but struggle to model long-range sequential information effectively, limiting attack efficiency and generalization. In this paper, we propose a hybrid deep neural network architecture that integrates Residual Mamba blocks with multi-layer perceptrons (MLP) to enhance the modeling of side-channel information from AES implementations.

Lattice-Based Certificateless Proxy Re-Signature for IoT: A Computation-and-Storage Optimized Post-Quantum Scheme.

Proxy re-signature enables transitive authentication of digital identities across different domains and has significant application value in areas such as digital rights management, cross-domain certificate validation, and distributed system access control. However, most existing proxy re-signature schemes, which are predominantly based on traditional public-key cryptosystems, face security vulnerabilities and certificate management bottlenecks. While identity-based schemes alleviate some issues, they introduce key escrow concerns.

SBCS-Net: Sparse Bayesian and Deep Learning Framework for Compressed Sensing in Sensor Networks.

Compressed sensing is widely used in modern resource-constrained sensor networks. However, achieving high-quality and robust signal reconstruction under low sampling rates and noise interference remains challenging. Traditional CS methods have limited performance, so many deep learning-based CS models have been proposed.

A Lightweight Certificateless Authenticated Key Agreement Scheme Based on Chebyshev Polynomials for the Internet of Drones.

The Internet of Drones (IoD) overcomes the physical limitations of traditional ground networks with its dynamic topology and 3D spatial flexibility, playing a crucial role in various fields. However, eavesdropping and spoofing attacks in open channel environments threaten data confidentiality and integrity, posing significant challenges to IoD communication. Existing foundational schemes in IoD primarily rely on symmetric cryptography and digital certificates.

Identity-Based Provable Data Possession with Designated Verifier from Lattices for Cloud Computing.

Provable data possession (PDP) is a technique that enables the verification of data integrity in cloud storage without the need to download the data. PDP schemes are generally categorized into public and private verification. Public verification allows third parties to assess the integrity of outsourced data, offering good openness and flexibility, but it may lead to privacy leakage and security risks.

Role of risk factors and their variable types in predicting noise-induced hearing loss using artificial intelligence algorithms.

Early prediction and warning of occupational noise-induced hearing loss (NIHL) in workers is critical. This study aimed to explore the role of risk factors and their variable types to NIHL prediction through machine learning (ML) techniques. Data on exposure and NIHL were sourced from the Chinese National Occupational Disease Surveillance Programs and field measurements involving 15,160 workers.

The effects of time and money scarcity on regulatory focus: The mediating role of sense of control.

Regulatory focus involves two motivational systems: the promotion focus, aimed at achieving positive outcomes through proactive behaviors, and the prevention focus, centered on avoiding negative outcomes. These systems have been widely studied and influenced by factors such as resource scarcity. To conduct a comparative study of the effects of time and money scarcity on regulatory focus, this study used a questionnaire study and two experimental studies.

Lightweight identity authentication and key agreement scheme for VANETs based on SSL-PUF.

With the rapid development of smart cities and intelligent vehicles, the communication scale in Vehicular Ad-Hoc Networks (VANETs) is expanding significantly. Simultaneously, due to the low latency, and security requirements of the Internet of Vehicles (IoV), there is an increased demand for the performance of vehicle access authentication and key agreement protocols. Traditional identity authentication and key agreement protocols less frequently consider the impact of efficiency, and they use complex computations, which result in increased communication costs and reduced efficiency.

IAESR: IoT-oriented authenticated encryption based on iShadow round function.

With the growing popularity of the Internet of Things (IoT) devices and the widespread application of embedded systems, the demand for security and resource efficiency in these devices is also increasing. Traditional authenticated encryption (AE) algorithms are often unsuitable for lightweight devices due to their complexity and resource consumption, creating a need for lightweight AE algorithms. Lightweight devices typically have limited processing power, storage capacity, and energy resources, which necessitates the design of simple and efficient encryption algorithms that can function within these constraints.

Clinical characteristics and risk factors of esophageal reflux hypersensitivity: A multicenter study.

Background: Reflux hypersensitivity (RH) constitutes roughly 14% of patients with heartburn and 34% of those with refractory heartburn, yet it is inadequately comprehended.

Aim: To investigate the clinical characteristics and risk factors associated with RH.

Methods: We conducted a retrospective analysis of 109 patients with RH and 384 healthy controls from three medical centers between January 2022 and December 2023.

SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising.

In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly in multimedia applications. In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction.

A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning.

The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models' accuracy. Many studies have sought to improve model performance through feature selection. However, a single feature selection method has incomplete, unstable, or time-consuming shortcomings.

The motivational factor of working to involve outdoor sport as mediated by health awareness and social influence: a case study for China and Malaysia.

Objectives: This study examines the motivating factors influencing outdoor sports participation in China and Malaysia, emphasizing the mediating roles of social influence and health consciousness, and their implications for policy and community engagement.

Study Design: A mixed-methods approach was employed in this cross-sectional study, integrating quantitative surveys and qualitative focus group discussions to capture diverse perspectives on outdoor sports participation and its motivating factors.

Methods: Data were collected from 500 individuals using self-administered questionnaires assessing motivation, social influence, health awareness, and participation frequency.

Few-shot traffic classification based on autoencoder and deep graph convolutional networks.

Traffic classification is a crucial technique in network management that aims to identify and manage data packets to optimize network efficiency, ensure quality of service, enhance network security, and implement policy management. As graph convolutional networks (GCNs) take into account not only the features of the data itself, but also the relationships among sets of data during classification. Many researchers have proposed their own traffic classification methods based on GCN in recent years.

PIA-A secure and efficient identity authentication scheme in telemedicine via the PUF method.

With the rise of IoT technology, remote medical services have gained increasing attention. These services became particularly popular during the COVID-19 pandemic due to their convenience, privacy, and ability to transcend geographical boundaries. In remote medical networks, identity authentication protocols are a crucial component, playing a vital role in ensuring that healthcare providers can operate in a secure and efficient environment.

SSM-Net: Enhancing Compressed Sensing Image Reconstruction with Mamba Architecture and Fast Iterative Shrinking Threshold Algorithm Optimization.

Compressed sensing (CS) is a powerful technique that can reduce data size while maintaining high reconstruction quality, which makes it particularly valuable in high-dimensional image applications. However, many existing methods have difficulty balancing reconstruction accuracy, computational efficiency, and fast convergence. To address these challenges, this paper proposes SSM-Net, a novel framework that combines the state-space modeling (SSM) of the Mamba architecture with the fast iterative shrinking threshold algorithm (FISTA).

Research on time-division multiplexing for error correction and privacy amplification in post-processing of quantum key distribution.

The post-processing of quantum key distribution mainly includes error correction and privacy amplification. The error correction algorithms and privacy amplification methods used in the existing quantum key distribution are completely unrelated. Based on the principle of correspondence between error-correcting codes and hash function families, we proposed the idea of time-division multiplexing for error correction and privacy amplification for the first time.

FusionOpt-Net: A Transformer-Based Compressive Sensing Reconstruction Algorithm.

Compressive sensing (CS) is a notable technique in signal processing, especially in multimedia, as it allows for simultaneous signal acquisition and dimensionality reduction. Recent advancements in deep learning (DL) have led to the creation of deep unfolding architectures, which overcome the inefficiency and subpar quality of traditional CS reconstruction methods. In this paper, we introduce a novel CS image reconstruction algorithm that leverages the strengths of the fast iterative shrinkage-thresholding algorithm (FISTA) and modern Transformer networks.

A Conditional Privacy-Preserving Identity-Authentication Scheme for Federated Learning in the Internet of Vehicles.

With the rapid development of artificial intelligence and Internet of Things (IoT) technologies, automotive companies are integrating federated learning into connected vehicles to provide users with smarter services. Federated learning enables vehicles to collaboratively train a global model without sharing sensitive local data, thereby mitigating privacy risks. However, the dynamic and open nature of the Internet of Vehicles (IoV) makes it vulnerable to potential attacks, where attackers may intercept or tamper with transmitted local model parameters, compromising their integrity and exposing user privacy.

EWT: Efficient Wavelet-Transformer for single image denoising.

Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a more resource-efficient Transformer-based image denoising method that maintains high performance. To this end, we propose an Efficient Wavelet Transformer (EWT), which incorporates a Frequency-domain Conversion Pipeline (FCP) to reduce image resolution without losing critical features, and a Multi-level Feature Aggregation Module (MFAM) with a Dual-stream Feature Extraction Block (DFEB) to harness hierarchical features effectively.

Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs.

Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles.

PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices.

Electronic tickets (e-tickets) are gradually being adopted as a substitute for paper-based tickets to bring convenience to customers, corporations, and governments. However, their adoption faces a number of practical challenges, such as flexibility, privacy, secure storage, and inability to deploy on IoT devices such as smartphones. These concerns motivate the current research on e-ticket systems, which seeks to ensure the unforgeability and authenticity of e-tickets while simultaneously protecting user privacy.

GAN-based medical image small region forgery detection via a two-stage cascade framework.

Using generative adversarial network (GAN) Goodfellow et al. (2014) for data enhancement of medical images is significantly helpful for many computer-aided diagnosis (CAD) tasks. A new GAN-based automated tampering attack, like CT-GAN Mirsky et al.

An Access Control Scheme Based on Blockchain and Ciphertext Policy-Attribute Based Encryption.

Ciphertext policy-attribute-based encryption (CP-ABE), which provides fine-grained access control and ensures data confidentiality, is widely used in data sharing. However, traditional CP-ABE schemes often choose to outsource data to untrusted third-party cloud service providers for storage or to verify users' access rights through third parties, which increases the risk of privacy leakage and also suffers from the problem of opaque permission verification. This paper proposes an access control scheme based on blockchain and CP-ABE, which is based on multiple authorization centers and supports policy updating.