Daoyu Lin
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Harnessing LLMs for multi-dimensional writing assessment: Reliability and alignment with human judgments Open
Recent advancements in natural language processing, computational linguistics, and Artificial Intelligence (AI) have propelled the use of Large Language Models (LLMs) in Automated Essay Scoring (AES), offering efficient and unbiased writin…
Incorporating Fine-Grained Linguistic Features and Explainable AI into Multi-Dimensional Automated Writing Assessment Open
With the flourishing development of corpus linguistics and technological revolutions in the AI-powered age, automated essay scoring (AES) models have been intensively developed. However, the intricate relationship between linguistic featur…
SatelliteRF: Accelerating 3D Reconstruction in Multi-View Satellite Images with Efficient Neural Radiance Fields Open
In the field of multi-view satellite photogrammetry, the neural radiance field (NeRF) method has received widespread attention due to its ability to provide continuous scene representation and realistic rendering effects. However, the sate…
Multivariate Network Layout Using Force-Directed Method with Attribute Constraints Open
Graph visualization with proper layout is widely applied to understand the relationship between entities in a complex system and the topological structure information is mainly used. Real-world graphs often have the community structures pr…
Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification Open
Deep learning-based methods succeed in remote sensing scene classification (RSSC). However, current methods require training on a large dataset, and if a class does not appear in the training set, it does not work well. Zero-shot classific…
Towards a Reliable Evaluation of Local Interpretation Methods Open
The growing use of deep neural networks in critical applications is making interpretability urgently to be solved. Local interpretation methods are the most prevalent and accepted approach for understanding and interpreting deep neural net…
DyEgoVis: Visual Exploration of Dynamic Ego-Network Evolution Open
Ego-network, which can describe relationships between a focus node (i.e., ego) and its neighbor nodes (i.e., alters), often changes over time. Exploring dynamic ego-networks can help users gain insight into how each ego interacts with and …
Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding Open
As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its loc…
Deep Discriminative Representation Learning with Attention Map for Scene Classification Open
In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature …
Where is the Model Looking At? – Concentrate and Explain the Network Attention Open
Image classification models have achieved satisfactory performance on many datasets, sometimes even better than human. However, The model attention is unclear since the lack of interpretability. This paper investigates the fidelity and int…
A Question Answering-Based Framework for One-Step Event Argument Extraction Open
Event argument extraction, which aims to identify arguments of specific events and label their roles, is a challenging subtask of event extraction. Previous approaches solve this problem in a two-stage manner that first extracts named enti…
Deep Discriminative Representation Learning with Attention Map for Scene Classification Open
Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approach…
A Remote Sensing Image Dataset for Cloud Removal Open
Cloud-based overlays are often present in optical remote sensing images, thus limiting the application of acquired data. Removing clouds is an indispensable pre-processing step in remote sensing image analysis. Deep learning has achieved g…
Edge-Nodes Representation Neural Machine for Link Prediction Open
Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universa…
A Graph Layout Framework Combining t-Distributed Neighbor Retrieval Visualizer and Energy Models Open
Graph layout investigates the structure of the graph in order to better obtain the information implied in the graph. To solve the shortcomings of dimension reduction layouts on local adjustment and the insufficiency of energy models to mai…
IES-Backbone: An Interactive Edge Selection Based Backbone Method for Small World Network Visualization Open
Visualization of the small world network is an excellent challenge for classic layout algorithm, which is highly connected, resulting in the shape of the hairball. Backbone extraction method can simplify the classic layout to get better vi…
Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration Open
The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in or…
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network Open
Single image super-resolution is an effective way to enhance the spatial\nresolution of remote sensing image, which is crucial for many applications such\nas target detection and image classification. However, existing methods based\non th…
Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network Open
Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such a…
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification Open
With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learnin…