bluuebunny/crossref_metadata_2025_split huggingface dataset:
A semantic segmentation network based on CNN and Swin transformer for landslide detection
Authors: Dai Xiangyan , Li Lin 
Published: October 2024

Abstract
To address the issues of poor object localization, difficulty in object recognition, and inadequate segmentation performance of CNN-based semantic segmentation methods in landslide detection, a semantic segmentation model based on the Swin transformer and ResUNet architectures for landslide detection in remote sensing images is proposed in this paper. This method combines the global feature capabilities of the Swin transformer with the local feature extraction abilities of CNN and integrates our proposed RCCT module for landslide segmentation in remote sensing images. We apply this model to the identification and extraction of landslide disasters in Menyuan County and the Sanjiangyuan region of Qinghai Province, China, achieving a promising F1 Score of 81.91. Furthermore, we conducted a comprehensive evaluation of our proposed model on the BiJie Landslide dataset in comparison to four recently developed methods for landslide recognition. The experimental findings indicate the superior performance and effectiveness of our model over the comparative approaches.
Lightweight UAV target detection algorithm
Authors: Zhang Xiaozheng , Bao Dayang , Zhao Haili , Wang Qi 
Published: October 2024

Abstract
Aiming at the public area UAV detection task, the target is easy to occlude, the scale variation is large, and the existing detection algorithm model parameters are large. In this paper, the lightweight network model YOLOv5s-UD is designed for the low-altitude UAV target detection algorithm. In this detection model, the proposed approach employs the streamlined Shufflenetv2 architecture to enhance the core network. To optimize the UAV’s relevant feature data, the CBAM attention module is integrated during the feature amalgamation phase. Furthermore, the GSConv is implemented to upgrade conventional convolution within the neck network, ensuring a reduction in model parameters without compromising detection precision. Experimental outcomes reveal a 36.2% computational reduction compared to the YOLOv5s algorithm, with an average detection accuracy of 84.1%. This demonstrates the algorithm’s capability to efficiently detect UAV targets while minimizing computational loads, thereby exhibiting significant practical significance.
Research on traffic sign detection model with improved YOLOv8s
Authors: Wang Qingsong , Qiao Yanping , Li Jiaen , Wang Ai 
Published: October 2024

Abstract
With the rapid advancement of intelligent driving technologies such as autonomous driving and driver assistance, the significance of traffic sign recognition in road and traffic safety has become increasingly prominent. Addressing the challenges posed by the current mainstream traffic sign detection models, which include complex background structures and the detection of small targets, this study proposes an enhanced network for detecting traffic signs based on YOLOv8s. Firstly, we modify the convolutional component of the backbone by replacing the initial convolutions with the Alterable Kernel Convolution (AKConv) network, thereby enhancing feature extraction efficiency and network performance. Secondly, we introduce the Global Attention Mechanism (GAM) within the network to effectively aggregate features, improve reconstruction performance, and maintain low computational and storage costs. Lastly, we integrate the Bi-directional Feature Pyramid Network (BiFPN) into the YOLOv8s framework, enabling the model to capture multi-scale features efficiently and enhance recognition accuracy. Experimental results conducted on the CCTSDB-2021 show the efficacy of the study improved YOLOv8s model. Precision, recall, and mAP@50% achieve impressive values of 96.0%, 79.4%, and 88.3%, respectively. These metrics represent a 4.6% improvement in precision, a 0.4% enhancement in the recall, and a 2% enhancement in mAP@50%, compared with the original YOLOv8s model. Moreover, this improved model exhibits greater precision when compared with other existing models.
Design and experiment of Beidou satellite navigation railway tunnel coverage enhancement system
Authors: Pan Peifen , Zhang Jifeng , Tao Tao , Liu Yangxue , Feng Boqing 
Published: October 2024

Abstract
In areas of satellite navigation signals being obstructed, coveraging the interior of the tunnel with Satellite navigation signals based on navigation signals real-time regeneration technology is an important way to achieve the applications of satellite navigation in railway mobile equipment positioning and auxiliary protection for operators. We analyzed the application requirements on navigation positioning and time synchronization, and technical requirements on universality and electromagnetic compatibility, etc. The article designed the composition and logical architecture of the Beidou satellite navigation railway tunnel coverage enhancement system, proposed the design of system functions and performance parameters, and verified the system’s functionality and performance through static mode testing and dynamic mode testing. The design and implementation of the Beidou satellite navigation railway tunnel coverage enhancement system have important practical value for the application in mobile equipment positioning and auxiliary protection for operators in railway tunnel.
A speech separation model improved based on Conv-TasNet network
Authors: Shi Hongwei , Wu Shiqi , Ye Minghao , Ma Changda 
Published: October 2024

Abstract
In the field of single-channel speech separation, the extraction and separation of features from mixed audio have always been the focus and difficulty of research. Currently, mainstream methods mainly suffer from poor generalization ability and issues such as inadequate feature extraction, which leads to the models’ inferior separation capability. This paper proposes an improved DConv-TasNet network model, focusing on the optimization of the encoder/decoder modules and separation modules and utilizing deep dilated encoders/decoders to extract features from mixed speech signals. It enhances feature extraction capability and generalization compared to conventional encoders/decoders. In terms of the separation module, improvements were made to the convolutional blocks within the module by enhancing feature extraction in the channel dimension, leading to improved performance of the separation network. Validation of the model’s performance was conducted using the WSJ0-Mix2 dataset, demonstrating superior performance compared to the Conv-TasNet network.
Disentangled traffic forecasting via efficient graph neural network
Authors: Zhang Wenyu , Zheng Kaidong 
Published: October 2024

Abstract
Traffic prediction is important to intelligent transportation systems, but it poses significant challenges due to the complex spatio-temporal correlation of traffic data. To address these challenges, we decompose complex traffic time series into events and trends using discrete wavelet transform. However, the high-frequency information decomposed by wavelet transform contains noise, so we designed a module to denoise the high-frequency events. We model the spatio-temporal correlations of trends and events separately using two mutually independent spatio-temporal networks. We use causal dilation convolution with local receptive fields and attention with global receptive fields to capture the correlation of fluctuating time series and the correlation of stable time series, respectively. We capture spatial correlation using a GNN optimized by an attention mechanism. Finally, we merge useful information from volatile events into predictable trends using adaptive event fusion. Our model significantly outperforms nine existing traffic prediction methods across four real datasets.
An enhanced seagull algorithm for multi-threshold image segmentation based on Kapur entropy
Authors: He Hanyue 
Published: October 2024

Abstract
To address the shortcomings of the Seagull Optimization Algorithm (SOA), such as poor distribution of individuals’ positions and low population diversity leading to susceptibility to local optima after the collision avoidance phase, an Enhanced Seagull Algorithm (ESOA) is proposed. Firstly, a chaos-guided mechanism is utilized to replace the original individual positions after collision avoidance with the mean of individual optima and current positions, effectively improving the distribution of the population. Secondly, a circular-topology neighborhood structure with random weights is introduced, allowing interaction between the current individual’s position and the information from its two adjacent optimal positions, thereby improving population clustering and enhancing diversity. Thirdly, a dynamic lens mapping strategy is employed to direct the optimal seagull individuals toward their newly generated mapped points, enhancing their ability to avoid local optima. ESOA is applied to the problem of multi-threshold image segmentation based on Kapur entropy, and experimental results demonstrate its effectiveness in improving segmentation performance.
Human behavior recognition method based on wearable devices
Authors: Zhang Wei , Yu Guibo , Deng Shijie 
Published: October 2024

Abstract
Human behavior recognition remains a prevalent subject of inquiry in contemporary scientific studies. Behavior recognition technology has penetrated into every aspect of our lives, mainly in video surveillance, health monitoring, and smart homes. Aiming at some people who need behavioral monitoring to prevent danger, a human behavior recognition method based on wearable devices is proposed, which first acquires the three-axis acceleration data of behavioral activities of such people through wearable devices and then performs sliding window segmentation and feature extraction on the acquired data and finally inputs the obtained features into the dual-directional long and short term memory framework (BiLSTM) model to complete the identification of human behavior. To affirm the method’s reliability, we performed activity detection tests on a dataset from UCI featuring a non-powered wearable sensor used by the senior population. The outcomes indicate that the method is proficient in identifying the routine daily activities of this demographic, demonstrating its practical utility.
Diffusion-Cap: A diffusion model for image captioning
Authors: Xiao Tingxuan , Yang Yizhe , Gao Yang 
Published: October 2024

Abstract
While autoregressive image caption generation models have achieved remarkable success, they are still constrained in generation speed, which may become a bottleneck in practical applications. To address this challenge, our research introduces the Diffusion Image Captioning Model (Diffusion-Cap). This innovative, non-autoregressive framework conceptualizes image captioning as a process that unifies continuous and discrete diffusion. By exploring two distinct architectural designs, our research investigates optimal strategies for the image captioning diffusion process. Comprehensive assessments on the MSCOCO dataset confirm the superior performance of our Diffusion-Cap, surpassing existing non-autoregressive benchmarks in crafting accurate captions.
An entity alignment method with attribute augmentation and contrastive learning
Authors: Yang Cheng , Zhang Chunxia , Chen Yihao 
Published: October 2024

Abstract
The entity alignment(EA) task is to identify entities with the same semantics in the knowledge graph(KG), an essential issue in KG fusion and big data mining. Existing entity alignment methods mainly adopt graph embedding-based methods. However, they still have some shortcomings. First, they heavily rely on high-quality alignment seed and external semantic information. Secondly, the present attention mechanism focuses on the entire graph information, neglecting the noise of attribute information. This paper proposes an EA approach based on Attribute Augmentation and Contrastive Learning (AACL). Our method introduces attribute augmentation to enhance the structure information of knowledge graphs and reduce dependence on alignment seed. A masked attention mechanism is developed to emphasize important attribute information and mask out invalid attributes to better capture semantic dependencies in the KG. Experimental results on three public datasets indicate that our AACL outperforms the present entity alignment approaches.
Divergent future trends in global land greening and carbon sequestration under climate change scenarios
Authors: Bian Chenyu , Xia Jianyang 
Published: October 2024

Abstract
Over the past four decades, global land greening has promoted carbon (C) storage in terrestrial ecosystems. However, whether the future trajectory of this positive greening effect on ecosystem C sequestration is sustainable under various climate scenarios remains uncertain. Here, using projections from ten Earth system models, we found divergent trends in the relationship between global land greening and C storage among three distinct shared socioeconomic pathways (SSPs). We identified global transition times for their positive relationships, which will occur in 2034, 2038, and 2048 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. We found a widespread decoupling trend of vegetation greening and ecosystem C storage under medium to high emission scenarios, particularly in the tundra, boreal forest, and grassland regions, by delving deeper into six transition modes. These findings underscore the highly uncertain role of vegetation greening in land C sequestration under prospective climate change scenarios.
Management Effectiveness Evaluation using the Bayesian Belief Network Approach: A case study of Cu Lao Cham Marine Protected Area, Vietnam
Authors: Khuu D T 
Published: October 2024

Abstract
The Kunming-Montreal Global Biodiversity Framework set an ambitious target of safeguarding 30% of the Earth’s land and sea through well-managed Protected Areas (PAs) by 2030. Despite 196 countries committing to expand PA coverage, a lack of evidence on existing PA effectiveness and limited data on human-ecosystem interactions pose formidable challenges to implementation and prioritising conservation funding. Within this context, this paper aims to test a simple and user-friendly model, which is based on Bayesian Belief Networks, in evaluating the management effectiveness of Cu Lao Cham Marine Protected Area (CLC-MPA) in Vietnam. It focuses on assessing whether the MPA is effective in achieving its conservation objectives and if the target resources can endure the current pace of economic development. The study underscores the importance of prioritising eco-tourism and sustainable local economic models amid the intense forces of mass tourism development. It argues that tourism carrying capacity for CLC should be estimated at the outset of sustainable tourism development plan. Besides, there is a need for an integrated approach to MPA management to include legal provisions to address the impacts of tourism and pollution. Also, given the increasing pressure from incoming fishers, it is essential for increasing trans-provincial coordination for better enforcement of MPA regulations.
Understanding sustainable tourist behaviours of Vietnamese visitors in Ho Chi Minh City, Vietnam
Authors: Tung N T T , Thu T N M , San N D , Ngoc P T B 
Published: October 2024

Abstract
Sustainable tourism is an important goal within the global sustainable development framework. In sustainable tourism, tourists are expected to behave in an environmentally friendly manner and respect the local culture and economy. However, the level of sustainable behaviour among tourists varies across different regions and countries. This study aims to understand the sustainable tourism behaviour of Vietnamese tourists visiting Ho Chi Minh City, Vietnam. It focuses on (1) the sustainable behaviour of tourists, (2) the correlation between access to sustainable tourism information and sustainable behaviour, as well as participant characteristics and the behaviour; and (3) initial suggestions for promoting changes towards sustainable behaviour among tourists. The study uses a mixed-methods approach, combining both quantitative and qualitative methods. Quantitative data were collected through direct questionnaires from 240 tourists visiting Ho Chi Minh City. Qualitative data were obtained through semi-structured, in-depth interviews with 47 participants. The results indicate three common groups of behaviour related to sustainable tourism during travel: saving electricity and water, choosing public transportation, and food-related behaviour. Specifically, the study found a correlation between access to sustainable tourism information and sustainable behaviour, including the choice of transportation (sig = 0.040) and food-related behaviour (sig = 0.006). Surprisingly, the study did not find a correlation between access to sustainable tourism information and behaviour related to conserving electricity and water at destination accommodations. The findings also revealed that well-educated young Vietnamese travellers (aged 18-35) earning high salaries, living in urban areas, and currently employed by international companies have a better understanding of sustainability, leading to increased sustainable practices within the tourism industry. Finally, the study suggests that strategies should shift towards changing behaviour related to saving electricity and water through improvements in hotel infrastructure and management practices rather than focusing solely on engaging sustainable tourist behaviour through providing sustainable information.
Impact of the Urbanization Process on the Subsidence Phenomena Using Remote Sensing in the Inner Districts of Ho Chi Minh City, Vietnam
Authors: Ha T C , Nguyen T P C , Tran H P , Do V T 
Published: October 2024

Abstract
Subsidence in Ho Chi Minh city (HCM city) occurs in many places, the current reason no longer comes from natural disasters but mainly from human activities (groundwater exploitation, ground improvement,…). Especially in innercity districts, during high tide or heavy rain, many areas are locally flooded, concentrated in places with high construction density. To monitor the subsidence phenomenon in urban areas, the advent of remote sensing technology is an effective tool, helping researchers and managers capture information quickly, accurately and continuously. In this study, the DInSAR method was used to determine the rate of subsidence in urban districts of HCM city. DInSAR is a popular method for estimating land displacement based on the principles of Interferometric Synthetic Aperture Radar (InSAR) using different time series data. We used C-band SAR Sentinel 1 data in the period 2015 - 2023 to analyze subsidence, the results showed that subsidence hot spots were concentrated in District 7, Binh Thanh District and part of Go Vap District at high speeds from 2 - 3 cm/year. Over time, the phenomenon of subsidence with a value of > 2cm/year was distributed in Binh Tan and Tan Phu districts following the trend of new urban expansion to the West of HCM city. Additionally, Sentinel 2 imaging data was also used in this study to demonstrate that areas with high construction density have high subsidence rates. From there, it can be seen that the urbanization process is one of the causes of subsidence in HCM city.
Particle migration due to non-uniform laminar flow
Authors: Curt Koenders M A , Petford Nick 
Published: October 2024

Abstract
Using methods of granular mechanics in the quasi-static limit, with inter-particle interactions derived from the lubrication limit, the intensity of velocity fluctuations in the slurry is associated with fluctuations in the local distribution of inter-particle distances. These are shown to consist of a vector intensity and a scalar intensity; the former couples to the first velocity gradient, the latter (which is associated with solidosity fluctuations) couples to the second velocity gradient. Rheologies for both are presented, as is the rheology that links the particle pressure to the intensity of the velocity fluctuations (also known as the ‘granular temperature’) to the dispersive pressure. The rheologies are informed by experimental results. The granular temperature profile, modified from previous work, is responsible for axial particle migration (Bagnold effect). Two broad categories are assessed: symmetrical vertical and non-symmetrical lateral flow. For the latter the roughness of the boundary walls and a non-zero density contrast are important; this case is studied for a system in which flow effects are confined to the immediate vicinity of the boundary. Sensitivity analysis reveals several key variables including the parameters that control a slipping boundary condition and the mean solidosity in the conduit. For lateral flow, a sedimentary deposit with a solidosity profile may develop near the upper or lower boundary. The theory predicts an approximate relation between the fluid-particle density contrast and sediment thickness as a function of the mean flow rate, conduit width, the mean particle diameter and fluid viscosity that has utility in a range of engineering and geological situations where particulate matter is transported in the laminar flow regime.
Quantum Onsager relations
Authors: Tsang Mankei 
Published: October 2024

Abstract
Using quantum information geometry, I derive quantum generalizations of the Onsager rate equations, which model the dynamics of an open system near a steady state. The generalized equations hold for a flexible definition of the forces as well as a large class of statistical divergence measures and quantum-Fisher-information metrics beyond the conventional definition of entropy production. I also derive quantum Onsager–Casimir relations for the transport tensors by proposing a general concept of time reversal and detailed balance for open quantum systems. The results establish a remarkable connection between statistical mechanics and parameter estimation theory.
Spike-based computational primitives to reproduce neural dynamics in the parietal cortex during motor preparation
Authors: Parrilla L , Filippini M , Zendrikov D , Fattori P , Indiveri G 
Published: October 2024

Abstract
Biologically plausible spiking neural network models of sensory cortices can be instrumental in understanding and validating their principles of computation. Models based on Cortical Computational Primitives (CCPs), such as Hebbian plasticity and Winner-Take-All (WTA) networks, have already been successful in this approach. However, the specific nature and roles of CCPs in sensorimotor cortices during cognitive tasks are yet to be fully deciphered. The evolution of motor intention in the Posterior Parietal Cortex (PPC) before arm-reaching movements is a well-suited cognitive process to assess the effectiveness of different CCPs. To this end, we propose a biologically plausible model composed of heterogeneous spiking neurons which implements and combines multiple CCPs, such as multi-timescale learning and soft WTA modules. By training the model to replicate the dynamics of in-vivo recordings from non-human primates, we show how it is effective in generating meaningful representations from unbalanced input data, and in faithfully reproducing the transition from motor planning to action selection. Our findings elucidate the importance of distributing spike-based plasticity across multi-timescales, and provide an explanation for the role of different CCPs in models of frontoparietal cortical networks for performing multisensory integration to efficiently inform action execution.