Reviewerly/exHarmony huggingface dataset:
TextGCL: Graph Contrastive Learning for Transductive Text Classification

Text classification is a fundamental task of natural language processing and has rich application scenarios. GNN models have recently attracted much attention due to their ability to encode complex graph-structured data. However, most of the works using GNNs for text classification only utilize static word vector models like word2vec and the encoding quality of node embeddings is mediocre, only few works explore the method of combining large-scale pretrained models with GNNs. Graph Contrastive Learning is a self-supervised learning method that can obtain high-quality node representations through pretraining without labeled data, which can be applied to downstream tasks such as node classification. In this work, we propose TextGCL, which introduces graph contrastive learning technique to transductive text classification task. TextGCL not only combines the ability of graph contrastive learning techniques to extract important information from graph structures, but also obtains the ability of large-scale pretrained models to extract rich information from text. In addition, transductive learning enables the model to jointly use unlabeled data (text only, no labels) and labeled data during the training stage, which greatly improves the performance of the entire model. We obtain state-of-the-art results on five widely used text classification benchmark datasets, empirically demonstrating the superiority of our model.
Domain Generalization and Feature Fusion for Cross-domain Imperceptible Adversarial Attack Detection

Deep learning-based imperceptible adversarial attack detection methods have recently seen significant progress. However, the accuracy, latency, and computational cost of previous methods remain insufficient. Particularly, trained attack detection models can potentially be applied in previously unseen conditions, such as new datasets or attacks for real-world applications. Therefore, to improve domain generalization performance, we propose a new method for cross-domain imperceptible adversarial attack detection by leveraging domain generalization, where we train the model's feature extractor or detector with a partner well-tuned for different domains. Different from conventional domain generalization methods, we use the global loss and local loss to train each feature extractor or detector. Moreover, to efficiently re-use high-resolution feature maps from the feature extractor, we propose a feature fusion network, which exploits feature maps from images that are attacked with different error rates and helps extract rich features to further improve the attack detection accuracy. Extensive experiments on four public datasets are used to demonstrate the efficacy of the proposed method. The source code of the proposed method is available at https://github.com/Yukino-3/DTAD.
Application of k-sigma Algorithm Based on Monte Carlo in Outlier Detection

Abstract Under the background of intelligent times based on information technology, the analysis and replacement of outliers become particularly critical for databases in the face of massive sequential data streams. In order to improve the effectiveness and practicability of the detection method, this paper determines the abnormal scores of data points in a specific data set by using the K-Sigma algorithm of the Monte Carlo method, adaptively adjusts the k value according to the abnormal scores, and marks the abnormal points. The advantages of the traditional k-sigma algorithm are fast operation speed and theoretical basis, while the disadvantages are that each index dimension is determined independently. However, in industrial production, due to the multi-index dimension of time series data, multiple size indicators of a data set are related to each other. Therefore, it is necessary to comprehensively consider whether an anomaly is abnormal by combining it with the data of other indicators. In addition, when there are no outliers in the data set, the traditional k-sigma algorithm is used to take the mean and variance of the data set as parameters. Due to the limitations of the data set itself, some data points will be mislabeled as outliers. Through the k-sigma algorithm based on the Monte Carlo method, we can effectively solve the above problems. The k-sigma algorithm based on the Monte Carlo method can generate a normal distribution according to the distribution of original data points, and extract a large quantity of data from the distribution to generate Monte Carlo data set. The Mahalanobis distance l i from each sample point to the mean in the Monte Carlo data set is calculated and compared with the Mahalanobis distance l j from the samples to be detected to the mean in the original data set. According to the number of sample points satisfying l i &lt; l j in the Monte Carlo data set, the value of the parameter k j is determined adaptively, and thus the outliers are determined. We implemented the k-sigma algorithm based on the Monte Carlo method through python, evaluated the effectiveness of the algorithm by accuracy, recall rate, and F1 score, and compared it with some machine learning algorithms. The results verified the feasibility and effectiveness of the algorithm, which can be used for real-time anomaly detection in the energy management database.
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning

Spatial information is essential in various fields. How to explicitly model according to the spatial location of agents is also very important for the multi-agent problem, especially when the number of agents is changing and the scale is enormous. Inspired by the point cloud task in computer vision, we propose a spatial information extraction structure for multi-agent reinforcement learning in this paper. Agents can effectively share the neighborhood and global information through a spatially encoder-decoder structure. Our method follows the centralized training with decentralized execution (CTDE) paradigm. In addition, our structure can be applied to various existing mainstream reinforcement learning algorithms with minor modifications and can deal with the problem with a variable number of agents. The experiments in several multi-agent scenarios show that the existing methods can get convincing results by adding our spatially explicit architecture.
SpA-Former:An Effective and lightweight Transformer for image shadow removal

In this paper, we propose an Effective and lightweight Transformer for image shadow detection and removal named SpA-Former to recover a shadow-free image from a single shaded image. In contrast to conventional methods that require two stages for shadow detection and then shadow removal, the SpA-Former is a one-stage network capable of learning the mapping function between shadows and no shadows, and does not require a separate shadow detection. SpA-Former is composed of Transformer encoder and CNN decoder, where the CNN decoder contains the GAN network. In the Transformer encoding stage, Gated Feed-Forward Network(GFFN) is devised to control the information flow. In the CNN decoding stage, Two-wheel RNN joint spatial attention(TWRNN) and Fourier transform residual block (FTR) are designed to achieve satisfactory results in shadow removal. The combination of Transformer and CNN is able to feed global features from the Vision Transformer encoder into CNN to enhance the global perception of CNN branches, taking into account the complementarity of local features and the global. The SpA-Former's inference speed is 0.0459s, and the final Parameters and FLOPS are only 0.47MB and 15G, achieving the current lightweight of image shadow removal. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/SpA-Former-shadow-removal
Improving small object detection with DETRAug

Small object detection is a challenge for computer vision models due to a shortage of image details, textures, and varying distances from the camera, resulting in objects of different scales and partial occlusion issues. In this paper, we present a new method for enhancing the robustness of image detection models using AUGMIX. Our approach involves applying various augmentations to the input images in a stochastic manner, resulting in a single output image after all transformations have been applied. In addition, we used the Jensen-Shannon loss to maintain a more stable model. In our experiments, we observed a decrease in the number of “no-object” detections, which refers to the detection of unrelated or background objects. The new approach was evaluated using the Deformable DETR, a model known for detecting small objects accurately, and compared to DETR and EfficientDet. We verified an improvement of at least 4.15% using the proposed technique and a more stable loss error.
Hybrid Domain Meta-Learning Network for Face Forgery Detection and Localization in Deepfakes

Existing face forgery detection methods often consider the manipulation detection problem as a binary classification problem, which are easily prone to overfitting. The detection performance degrades for datasets not appearing in the training stage. To solve this problem, we utilize the idea of domain generalization and design a hybrid domain meta-learning network (HDMNet) for face forgery detection and manipulation localization. The network allows the reveal of more essential face-swapping traces through multi-domain off-sets. Specifically, HDMNet consists of three modules, namely the feature extraction module (FEM), the face mask representation module (FMRM) and the meta-learning module (MLM). The FEM is composed of a comprehensive feature representation of image color features and high-frequency noise features. The FMRM utilizes graph convolution to predict face masks, providing supervision of domain knowledge. The MLM extracts domain-invariant features through a hybrid domain meta-learning strategy. Extensive experiments on several benchmark databases validate the effectiveness and good generalization ability of our method compared with several state-of-the-art methods.
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation

Few-shot relation extraction aims to recognize novel relations with few labeled sentences in each relation. Previous metric-based few-shot relation extraction algorithms identify relationships by comparing the prototypes generated by the few labeled sentences embedding with the embeddings of the query sentences using a trained metric function. However, as these domains always have considerable differences from those in the training dataset, the generalization ability of these approaches on unseen relations in many domains is limited. Since the prototype is necessary for obtaining relationships between entities in the latent space, we suggest learning more interpretable and efficient prototypes from prior knowledge and the intrinsic semantics of relations to extract new relations in various domains more effectively. By exploring the relationships between relations using prior information, we effectively improve the prototype representation of relations. By using contrastive learning to make the classification margins between sentence embedding more distinct, the prototype's geometric interpretability is enhanced. Additionally, utilizing a transfer learning approach for the cross-domain problem allows the generation process of the prototype to account for the gap between other domains, making the prototype more robust and enabling the better extraction of associations across multiple domains. The experiment results on the benchmark FewRel dataset demonstrate the advantages of the suggested method over some state-of-the-art approaches.
Angle based dynamic learning rate for gradient descent

In our work, we propose a novel yet simple approach to obtain an adaptive learning rate for gradient-based descent methods on classification tasks. Instead of the traditional approach of selecting adaptive learning rates via the decayed expectation of gradient-based terms, we use the angle between the current gradient and the new gradient: this new gradient is computed from the direction orthogonal to the current gradient, which further helps us in determining a better adaptive learning rate based on angle history, thereby, leading to relatively better accuracy compared to the existing state-of-the-art optimizers. On a wide variety of benchmark datasets with prominent image classification architectures such as ResNet, DenseNet, Efficient-Net, and VGG, we find that our method leads to the highest accuracy in most of the datasets. Moreover, we prove that our method is convergent.
MTCD-Model: A Two-Layer Model for Malicious Traffic Classification and Detection Based on Hierarchical Feature Learning

The rapid growth of cyber world and higher awareness of security in recent years have contributed to a significant demand in classification and detection of malicious traffic. Neural network is considered one of the effective methods. However, existing methods need to be improved. For example, the model performance is influenced by over dependance on manual design and extraction of feature. In addition, truncating or zero-complementing the traffic data results in loss of key traffic information or irrelevant input, which in turn affects the model performance in classifying and detecting malicious traffic. Motivated by these considerations and demands, this paper proposes a Malicious Traffic Classification and Detection Model (MTCD-Model), a two-layer model based on hierarchical feature learning. This model exploits both the CNN and Bi-SRU to learn the features of raw traffic data in indefinite length by hierarchical learning method, and achieve the classification and detection of malicious traffic with capsule network. The experimental results, based on the primary dataset TCD-2022, show that the F1-Score of MTCD-Model can reach 98.62, while the performance remains stable in different experimental scenarios. In addition, MTCD-Model generates different degrees of improvement in various evaluation metrics compared with the control model.
Arithmetical Evaluation System based on improved-YOLOv5 and CRNN networks

For primary school teachers, revising arithmetical exercises is labor-intensive and time-consuming task. To reduce their burdens, we propose a lightweight arithmetic evaluation system that can automatically assess arithmetic exercises. The designed system divides into two branches: detection and recognition. In the detection branch, a coordinate attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi-scale target recognition ability. Then, the complete IoU (CIoU) loss function leads to faster convergence and better performance on the detection results. The experimental results show that the detection accuracy of the proposed method improves mAP@.5-95(%) by 5.4% compared to the original algorithm. Moreover, the number of parameters and FLOPS decrease to 1.23M and 3.8G. In the recognition branch, we employ the improved-CRNN network to recognize arithmetical exercises, achieving an accuracy being up to 97.1 %.
Semantic Object Alignment and Region-Aware Learning for Change Captioning

The change captioning task, a downstream task of the image captioning task, is an emerging deep learning task. It aims to output sentences to describe the differences between two images, one of which is the original image and the other is the image changed from the original. Many current methods for generating disparity descriptions are based on the encoder-decoder model. The process is to first obtain the grid features using pre-trained ResNet on the image and encode the features, and then to obtain the sentences via a decoder. However, using traditional methods of encoding area features and applying them to grid features regardless can lead to performance degradation. Therefore, we propose a new model incorporating a novel design that is more robust to describe variations of image pairs, and a new addition of pseudo-region features that reduce the information missing from the grid features. Intensive experiments we have done can adequately demonstrate that our proposed model achieves state-of-the-art performance.
An homeostatic activity-dependent structural plasticity algorithm for richer input combination

This paper introduces a novel rate-based variant of homeostatic activity-dependent structural plasticity (HADSP) for echo state networks. Despite its importance in brain development, structural plasticity has been largely overlooked in artificial neural networks. Our algorithm, although using only homeostatic plasticity, let emerge principles of Hebbian learning. Our analysis sheds light on the information processing capabilities of HADSP-powered echo state networks and suggests that HADSP effectively leverages the inter-relationships of the network's inputs. The study highlights the potential for rate-based HADSP to contribute to the field of computational neuroscience and plasticity in echo state networks. Furthermore, our findings highlight the crucial role of structural plasticity in influencing network function and organization and contribute significantly to the ongoing research on leveraging plasticity for the advancement of reservoir computing techniques.
Isolated Korean Sign Gloss Recognition: The Phonological Base

In this study, we propose a chereme-based Sign Gloss Recognition (SGR) system based on the phonology of Korean Sign Language Grammar officially published by the National Institute of Korean Language. In order to evaluate the feasibility of the concept, we implement a protype test bed consisting of two different chereme recognition blocks and a keyframe detection block. Experiments using test bed demonstrate the capability of chereme-based SGR. In addition, we present the advantages of cherem-based SGR over gloss-based SGR, as well as research challenges derived from experimental results and analysis.
Residual Hybrid Attention Network for Single Image Dehazing

In hazy weather, there is an excess of gaseous and liquid particles in the air. Due to the scattering and absorption of these particles, images acquired by imaging devices typically exhibit degradation such as reduced contrast, brightness, and visibility, which affects the effectiveness of subsequent intelligent systems. To address this degradation, a large number of deep learning-based algorithms have been proposed to effectively remove haze. Recent studies have improved the feature learning capability of models with multi-scale fusion strategies and multi-branch networks. These complex structures improve the accuracy of haze removal, but come with some side effects, such as a larger number of parameters, increased memory consumption, and slower running speed. On the contrary, a single sequential network has a simple structure and a relatively small number of parameters. Furthermore, sequential operations can avoid the additional memory consumption caused by the large number of hierarchical features residing in memory. Different schemes attempt to use full-resolution and deeper networks to obtain better haze-free images, but also suffer from issues with parameters, memory, and running speed. To balance model performance and memory consumption, we propose a single image dehazing network based on encoder-decoder structure that integrates residual learning and attention mechanisms. The resolutions of feature maps are reduced by down-sampling, and the feature refinement module gradually recovers haze-free images at low resolution. Moreover, the receptive field of the model is increased by the large kernel design, which can capture global features and improve feature learning. The hybrid attention mechanism adaptively adjusts the spatial and channel weights so that the network will pay more attention to task-relevant objects and regions for accurate haze removal. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.
An Unsupervised Hierarchical Clustering Approach to Improve Hopfield Retrieval Accuracy

Despite its efficiency, the classical Hopfield network was a highly impractical data-searching solution due to its limited storage capacity. While the recently released modern Hopfield variant has increased its storage capacity, its searching ability is heavily affected by local minima and saddle points, which prevented it from becoming a worthy successor of the classical Hopfield network. We propose a novel unsupervised clustering approach to bypass local minima and saddle points to enhance the overall robustness of the Hopfield network. Our experimental results on benchmark MNIST indicate that our algorithm can increase the retrieval accuracy by over (20%) in general against the Hopfield Update Rule, proving that it is a far superior modelling solution.
TC-GAT: Graph Attention Network for Temporal Causality Discovery

The present study explores the intricacies of causal relationship extraction, a vital component in the pursuit of causality knowledge. Causality is frequently intertwined with temporal elements, as the progression from cause to effect is not instantaneous but rather ensconced in a temporal dimension. Thus, the extraction of temporal causality holds paramount significance in the field. In light of this, we propose a method for extracting causality from the text that integrates both temporal and causal relations, with a particular focus on the time aspect. To this end, we first compile a dataset that encompasses temporal relationships. Subsequently, we present a novel model, TC-GAT, which employs a graph attention mechanism to assign weights to the temporal relationships and leverages a causal knowledge graph to determine the adjacency matrix. Additionally, we implement an equilibrium mechanism to regulate the interplay between temporal and causal relations. Our experiments demonstrate that our proposed method significantly surpasses baseline models in the task of causality extraction.
Neural Text Classification by Jointly Learning to Cluster and Align

Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids. We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a variational autoencoder and interacting with distributional word embeddings, to enrich the text representation and measure the relatedness between tokens and each learnable cluster centroid. The proposed method jointly learns word clustering centroids and cluster-token alignments, achieving competitive results on multiple benchmark datasets and proving that the proposed cluster-token alignment mechanism is favorable to text classification. Notably, the learned text representations are well-clustered, which matches the ground-truth categories. Experimental results show that our model can also improve the classification performance on top of BERT representations. To the best of our knowledge, we are the first adopting the variational autoencoder to update clustering centroids for text classification.