The external appearance of maize seeds is one of important quality evaluation indicators for maize seeds. For traditional maize seed appearance quality detection, which mainly relies on naked eyes to inspect surface defects. Although computer vision as a relative mature way can be used for sample appearance quality inspection, manual feature extraction is still required. Meanwhile, machine learning technology, especially deep learning, has developed rapidly in the last decades. The maize seed surface defect detection coupled with deep learning can effectively replace traditional detection methods, reduce manual intervention, and decrease costs. In this paper, the collection and preprocessing of maize seed images, as well as the surface defects evaluation methods of maize seeds using a deep learning framework YOLOv5 were proposed. Firstly, in terms of image acquisition, maize seed batch surface defect detection system was established to obtain images. Then, the quality of maize seed images was improved by filtering, segmentation, and enhancement, which could significantly reduce noise in the images, separate the targets from the background and replace the background. Finally, ECA-Improved-YOLOv5S-Mobilenet model, which was established to improve the feature learning performance, could extracted the features from the maize seeds image and detect defects quickly at different levels. The experimental results showed that the precision was 92.8%, the recall rate was 98.9%, and the mPA0.5 was 95.5% with 8.8 MB of model size. In general, the proposed maize seeds surface defect detection method combined with deep learning could provide a theoretical support and technical basis for future development of seed grading and plantation. 

Detection of surface defects for maize seeds based on YOLOv5

Propagation models in social networks are critical, with extensive applications across various fields and downstream tasks. However, existing propagation models are often oversimplified, scenario-specific, and lack real-world user social attributes. These limitations detaching from real-world analysis lead to inaccurate representations of the propagation process in social networks. To address these issues, we propose a User Features Attention-based DAG-Aware Variational Autoencoder (DAVA) for propagation graph generation. First, nearly 1 million pieces of user attributes data are collected. Then DAVA can integrate the analysis of propagation graph topology and corresponding user attributes as prior knowledge. By leveraging a lightweight attention-based framework and a sliding window mechanism based on BFS permutations weighted by user influence, DAVA significantly enhances the ability to generate realistic, large-scale propagation data, yielding graph scales ten times greater than those produced by existing SOTA methods. Every module of DAVA has flexibility and extension that allows for easy substitution to suit other generation tasks. Additionally, we provide a comprehensive evaluation of DAVA, one focus is the effectiveness of generated data in improving the performance of downstream tasks. During the generation process, we discover the Credibility Erosion Effect by modifying the generation rules, revealing a social phenomenon in social network propagation. 

DAG-Aware Variational Autoencoder for Social Propagation Graph Generation

Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly, we discover that the spread of rumors is slower, persists longer, and, in most cases, involves fewer participants than that of non-rumors. And an undiscovered highlight is that reputable active users, termed `onlookers', inadvertently or unwittingly spread rumors due to their extensive online interactions and the allure of sensational fake news. Conversely, celebrities exhibit caution, mindful of releasing unverified information. Additionally, we identify cascade features aligning with exponential patterns, highlight the Credibility Erosion Effect (CEE) phenomenon in the propagation process, and discover the different contents and policies between the two platforms. Our findings enhance current understanding and provide a valuable statistical analysis for future research.

Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics

Change detection (CD) in remote sensing (RS) aims to consistently track alterations in specific regions over time. While current methods employ hierarchical architectures to analyze semantic details, they often miss crucial changes across different semantic levels, resulting in partial representations of environmental shifts. Addressing this, we propose AdaptFormer, uniquely designed to adaptively interpret hierarchical semantics. Instead of a one-size-fits-all approach, it strategizes differently across three semantic depths: employing straightforward operations for shallow semantics, assimilating spatial data for medium semantics to emphasize detailed interregional changes, and integrating cascaded depthwise attention for in-depth semantics, focusing on high-level representations. The experimental evaluations reveal that AdaptFormer surpasses many leading benchmarks, showcasing exceptional accuracy on LEVIR-CD and DSIFN-CD datasets. AdaptFormer showcases impressive performance with F1 and intersection over union (IoU) scores of 92.65% and 86.31% on the LEVIR-CD dataset, and 97.59% and 95.29% on the DSIFN-CD dataset, respectively. The datasets are available at https://github.com/aigzhusmart/AdaptFormer.

AdaptFormer: An Adaptive Hierarchical Semantic Approach for Change Detection on Remote Sensing Images

The topic of synthetic graph generators (SGGs) has recently received much attention due to the wave of the latest breakthroughs in generative modelling. However, many state-of-the-art SGGs do not scale well with the graph size. Indeed, in the generation process, all the possible edges for a fixed number of nodes must often be considered, which scales in $\mathcal{O}(N^2)$, with $N$ being the number of nodes in the graph. For this reason, many state-of-the-art SGGs are not applicable to large graphs. In this paper, we present SANGEA, a sizeable synthetic graph generation framework which extends the applicability of any SGG to large graphs. By first splitting the large graph into communities, SANGEA trains one SGG per community, then links the community graphs back together to create a synthetic large graph. Our experiments show that the graphs generated by SANGEA have high similarity to the original graph, in terms of both topology and node feature distribution. Additionally, these generated graphs achieve high utility on downstream tasks such as link prediction. Finally, we provide a privacy assessment of the generated graphs to show that, even though they have excellent utility, they also achieve reasonable privacy scores.

pdf/sangea-scalable-and-attributed-network-generation-5b3o9q9tyi.pdf

SANGEA: Scalable and Attributed Network Generation

Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties such as the existence of a link between a pair of nodes, and global aggregate graph-level statistics, such as motif counts. This paper proposes a new multi-level framework that jointly models node-level properties and graph-level statistics, as mutually reinforcing sources of information. We introduce a new micro-macro training objective for graph generation that combines node-level and graph-level losses. We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs. Our experiments show that adding micro-macro modeling to the GraphVAE model improves graph quality scores up to 2 orders of magnitude on five benchmark datasets, while maintaining the GraphVAE generation speed advantage.

pdf/micro-and-macro-level-graph-modeling-for-graph-variational-15bzt1jv.pdf

Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders

Irregularly, asynchronously and sparsely sampled multivariate time series (IASS-MTS) are characterized by sparse and uneven time intervals and nonsynchronous sampling rates, posing significant challenges for machine learning models to learn complex relationships within and beyond IASS-MTS to support various inference tasks. The existing methods typically either focus solely on single-task forecasting or simply concatenate them through a separate preprocessing imputation procedure for the subsequent classification application. However, these methods often ignore valuable annotated labels or fail to discover meaningful patterns from unlabeled data. Moreover, the approach of separate prefilling may introduce errors due to the noise in raw records, and thus degrade the downstream prediction performance. To overcome these challenges, we propose the time-aware dual attention and memory-augmented network (DAMA) with stochastic generative imputation (SGI). Our model constructs a joint task learning architecture that unifies imputation and classification tasks collaboratively. First, we design a new time-aware DAMA that accounts for irregular sampling rates, inherent data nonalignment, and sparse values in IASS-MTS data. The proposed network integrates both attention and memory to effectively analyze complex interactions within and across IASS-MTS for the classification task. Second, we develop the stochastic generative imputation (SGI) network that uses auxiliary information from sequence data for inferring the time series missing observations. By balancing joint tasks, our model facilitates interaction between them, leading to improved performance on both classification and imputation tasks. Third, we evaluate our model on real-world datasets and demonstrate its superior performance in terms of imputation accuracy and classification results, outperforming the baselines. 

Toward Learning Joint Inference Tasks for IASS-MTS Using Dual Attention Memory With Stochastic Generative Imputation.

: Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears, which cannot realize real-time detection of ear falling. The improved You Only Look Once-V4 (YOLO-V4) algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters. K -means clustering algorithm is used to obtain a prior box matching the size of the dropped ears, which improves the Intersection Over Union (IOU). Compare the effect of different activation functions on the accuracy of the YOLO-V4 model, and use the Mish activation function as the activation function of this model. Improve the calculation of the regression positioning loss function, and use the CEIOU loss function to balance the accuracy of each category. Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model. The channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the model. The final model size was only 10.77% before compression, and the test set mean Average Precision (mAP) was 93.14%. The detection speed was 112 fps, which can meet the need for real-time detection of maize harvester ears in the field. This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.

https://www.ijabe.org/index.php/ijabe/article/download/6660/pdf

Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ke Li

Author Tools

Chat about Author

Papers

Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders

Toward Learning Joint Inference Tasks for IASS-MTS Using Dual Attention Memory With Stochastic Generative Imputation.

Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4