Reviewerly/exHarmony huggingface dataset:
Comparative Study of Inductive Graph Neural Network Models for Text Classification

Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output.
The Effect of Time Pressure on Static and Dynamic Visual Search Performance Based on Eye Movement Data

Static visual search and dynamic visual search are two categories of visual search that have previously been studied separately. This study aimed to ascertain the effect of time pressure on both static and dynamic visual search performance. We conducted an experiment using eye tracking to study search performance under 5 kinds of time pressure. The results identified time pressure has different effects on static and dynamic visual search. The study confirmed that low time pressure promotes static visual search performance while high time pressure inhibits it. Besides when the time pressure is high, the differences between static and dynamic visual search performance are significant. The results show that time pressure can affect the performance of visual search, which can provide guidance on the timing of automated search in situations such as illegal items inspection in stations and airports and manual inspection of defective products in the factory.
An End-to-end Foreground Category Attention Boosting Network

In this paper, we introduce a foreground category attention boosting (CAB) framework for coarse-to-fine image segmentation, which makes full use of the coarse category information in low resolution layers. Our contributions can be summarized as: Firstly, we introduce the category attention mechanism to build the CAB module, which realizes a new stronger learner by aggregating the coarse category information and the feature maps of the current layer. Secondly, we propose an end-to-end foreground category attention boosting (FAB) neural network, which fully utilizes the complementary effects among different resolution feature maps and enhances the contribution from low resolution layers to fine segmentation with low computational cost. Thirdly, Extensive experiments on public dataset show that our approach can achieve superior performance over the state-of-the-art methods.
Deep Deterministic Policy Gradient (DDPG)-based Photovoltaic Power Forecasting

The uncertainty and randomness of large-scale photovoltaic (PV) generation will seriously affect the safety and economy of the power systems. In order to mitigate the negative impact of grid-connected solar PV, it is important to accurately predict PV power. In view of this, this paper first proposes a novel hybrid method based on non-pooling convolutional neural network and deep deterministic policy gradient (DDPG) for ultra-short-term PV power forecasting. The DDPG model is used to implement the feature training task, and the NPCNN model is introduced into DDPG’s actor network to learn action strategies for partially observed problems in complex environments. The proposed method is extensively evaluated on real PV data in Limburg, Belgium. Numerical results show that the proposed method can provide good forecasting performance in different seasons and forecasting horizons.
Deblurred Adversarial Defence For Object Tracking

It has become a consensus that deep convolutional neural network has poor resistance to attack. Blur attack is a usual attack method. Motion blur caused by fast moving is an important challenge in object tracking, which has a serious impact on the robustness and accuracy of tracking. The adversarial attack variant uses the generation principle of motion blur to attack a tracker leading to drift in the tracking process. In our work, a novel method is proposed to separate the input images after the tracker is attacked to obtain the target object and the background, which decomposes object positions and appearances in multiple consecutive sub-frames, and sharpens the appearances in the process. Then the target is embedded in the background, and the most appropriate result is selected as the new input to ensure the performance of the tracker. A self-supervised loss function is designed to improve the tracking performance and has a good generalization. After testing our algorithm on datasets, the experimental results prove that the proposed adversarial defense method plays a significant role in protecting the tracker after being attacked.
Learning fine-grained modality-invariant feature via Wasserstein Distance for Visible-Thermal Person Re-Identification

Visible-Thermal Person Re-identification(VT-ReID) focuses on matching query pedestrian images with the gallery sets, in which images of query and gallery sets are captured from different modalities. Compared with traditional person re- identification(Re-ID), VT-ReID concerns the additional cross- modality disparity caused by different spectrum cameras, in addition to intra-class variations. Existing VT-ReID methods commonly design the loss that is particularly for the global feature representations, which have limited the discriminability and ignored the relationship between the semantic parts. In this paper, we propose a novel Cross-modality Wasserstein Triplet Loss(CM-WTL) that can emphasize the semantic part and alleviate the modality discrepancy. This loss is based on the Wasserstein distance metric, which addresses the optimal transport problem by linear programming. It measures how much effort it would take to move the part-level embeddings to align two distributions, such that the distance between local features will be measured by obtaining an optimal transport matrix. In this manner, the obtained optimal transport matrix would assign different weights to relevant and irrelevant parts, resulting the alignment of local feature distribution. Therefore, our proposed CM-WTL can effectively mitigate the cross-modality discrepancy and learn the more fine-grained discriminative information. Extensive experimental results and comparisons demonstrate that our proposed method can achieve comparable performance with recent VT-ReID models.
Transfer Extreme Learning Machine with Cross Domain Mean Approximation Projection

Traditional extreme learning machine (ELM) with the advantages of fast speed and high precision, is widely used in pattern recognition and machine learning. However, it has two shortcomings: 1) Its accuracy decreases greatly under lack of available training samples. 2) The performance is greatly affected by the initialization of hidden-layer parameters. To solve the above two problems, we propose a novelty ELM named transfer extreme learning machine with cross domain mean approximation projection (TELM-CDMAP). It first uses cross domain mean approximation (CDMA) to project input data into feature subspace instead of the data transformation from input-layer to hidden-layer. Then, we apply a between-domain scatter matrix (BDSM) term into the hidden-layer and solve the optimal output weight with the ability of knowledge transfer. The classification experiments on Office+Caltech object recognition and USPS+MNIST digital handwriting datasets are carried out and the result show the effectiveness of our ELM models.
Analysis Platform of Student Learning Data Combined with Computer Vision

At present, most teaching platforms based on artificial intelligence are designed to launch learning tasks for students. However, those teaching platforms are less to analyze the knowledge mastery of students and less to carry out personalized and accurate teaching according to the learning situation. Teachers using those platforms generally check the learning situation of the whole student in a coarse granularity, which will lead to the lack of data analysis and learning situation of a single student, and therefore there is no feedback to adjust the teaching strategy. Those factors contribute to the insufficiency of communication and interaction between teachers and students. The effect of online teaching is inferior to offline teaching. In this paper, we proposed a learning platform that combines artificial intelligence. This platform aims to strengthen teachers' control over students' learning process and help teachers improve their teaching strategies. This paper combines deep learning technology with computer vision to collect data and analyze students' learning status, using machine algorithms to achieve identity binding and data feedback. Finally, we also established the environment for big data for the future expansion of data analysis functions.
CMRI Segmentation Domain Generalization by Random Style Transfer

When training data and test data have different distribution, performance of neural networks may decrease significantly, and model is difficult to generalize knowledge. Domain generalization (DG) attempts to learn general and transferable knowledge from a variety of source domain data to enable the model to generalize to unseen domains. In this paper, considering both DG and few-shot scenario, we propose a random stylization block to augment existing styles through nonlinear combination of data, called style escape, so as to obtain more unseen styles for the model to learn consistency. We propose a heuristic gradient weighting strategy to optimize the training process of meta-learning model. The experimental results on M&Ms dataset show that segmentation performance of our model is significantly improved compared with baselines, and the model training cost is noticeably reduced, which fully verifies the effectiveness of our method.
Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm

With the wide application of online examinations in colleges and universities, intelligent test paper generation technology has become a hot topic. In this paper, we propose an improved genetic simulated annealing algorithm (GSA) and apply it to the test paper generation process to improve the quality and speed of intelligent test paper generation. Firstly, we optimize the genetic operators in real number coding and introduce an adaptive adjustment strategy on the premise of maintaining individual diversity. Secondly, we apply simulated annealing to individuals of each generation. Finally, we conduct abundant experiments. The results show that the improved algorithm can promote the success rate of test paper generation and effectively enhance the quality of test paper.
AENet: End-to-end training of Attention Efficientnet for stroke segmentation

Medical image segmentation has extensive application and research value in medical research and practice fields, such as clinical diagnosis, pathological analysis, Operation Planning, image information processing, computer-assisted surgery, and so on. In the medical imaging of stroke, the lesions can be distributed in any area, and these lesions vary in size and shape. At present, various network frameworks have been used in deep learning for medical image segmentation, and good results have been achieved. However, the extremely small lesions in stroke images have serious neglect and false detection problems. To solve this problem, we design a loss function. This loss function optimizes the learning of semantic feature information around the target lesions, which can assist the network to locate small target regions more easily in the early stage of training. Meanwhile, it can add spatial information to assist the guidance network to achieve better convergence, accelerate network training, and finally improve the segmentation accuracy. In addition, we propose an end-to-end network structure, where the decoder adds the Attention Gates module to focus Attention on salient features that are useful for a particular task, the final prediction result is obtained by using the decoder structure to recover the spatial resolution. We sent the stroke images of different size lesions to the network for training by combining this network structure and the loss function we designed. The experimental results show that the DICE and NSD of our experimental data are improved effectively.
Reversible Data Hiding Using Convolutional Neural Network and Digital Signal Processing Techniques

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. Based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development.
Improving Emotion Classification by a Combination of Personal Texts and Social Big Data Based on Naive Bayes

Nowadays, a lot of personal texts are available due to the rise of social networking platforms. Using the texts, many research are conducted to estimate emotions. Especially, the tweets posted on Twitter are used in research employing machine learning as a training data for emotion analysis and classification. In the classification, it may be possible to improve the accuracy by using one’s own past texts as a training data. In this study, we combine personal texts and Twitter data as the training data and try to improve the accuracy of emotion estimation by using Naive Bayes. We obtain texts from one’s past diary. In addition, tweets are collected from similar attributes accounts. By an evaluator, the texts and tweets are classified into three categories: positive, negative, and neutral. Then, we set four patterns by changing the combination of personal texts and tweets. As a result, the highest average accuracy was obtained by weighting words that appeared in personal texts and combining them with Twitter data. It shows that it is important to focus on words that the person has used in the past in estimating his/her own emotions.
Feature Fusion Network Based on Academic Content Detection

At a time when academic achievements are being protected with emphasis the testing of academic contents has become a hot topic of much attention at present. In order to avoid the phenomenon of "multiple use of one image" and "image plagiarism" in the academic world, in view of the defects of insufficient feature fusion and low efficiency in the current target detection model, we propose a more powerful model method AIDPaper based on Faster R-CNN. AIDPaper adds PAFPN and DIoU Loss to the Faster R-CNN model. PAFPN is a bottom-up path added on top of FPN to greatly shorten the information propagation path and avoid information loss to a large extent. Subsequently, adaptive feature pooling is added to perform feature fusion for each regional proposal projected onto the feature layer to further enhance the fusion information of the features. DIoU loss provides the prediction direction for the prediction box making the regression more accurate not only speeding up the convergence but also reducing the detection time of the model. We perform experimental comparisons on the PaperImg dataset. The experimental results show that our proposed AIDPaper performs better and the mAP of AIDPaper is much higher than that of the baseline model Faster R-CNN at 3.2% ahead of Libra R-CNN at 6.1%. It has been shown experimentally that our model is more competitive compared to current detection models.
Dual-Attention Feature Decoupling in Siamese Networks for Object Tracking

Object tracking have been made significant progress in Siamese-based trackers, however, it is still difficult to track the target under scale variations, occlusions and unknown deformations. To solve this issue, we propose to decouple feature in tracking-heads by dual-attention to alleviate the conflict between the required features of classification and regression tasks. Specifically, we introduce channel attention to enhance high-level semantic feature for classification and adopt deformable attention to stress high-resolution local detail information for regression, separately. Besides, we propose multi-stage losses strategy to fuse multi-level features for effectively optimizing the classification and regression tasks after decoupling tracking-heads to improve tracking performance. Extensive experiments on visual tracking benchmarks including VOT2019 and OTB100, demonstrate that SiamDTH achieves state-of-the-art performance with a considerable real-time speed.
Detection of Abnormal Crowd Behavior Based on Graph Convolutional Neural Network

With the increasing demand for public safety precautions, the accuracy and timeliness of abnormal crowd behavior detection is gradually improving. Therefore, with the development of the technology for the abnormal crowd behavior, it’s even more demanding on how to effectively prevent the occurrence of accidents and eliminate the impact of disasters on the society. This article first introduces the current applications of abnormal behavior detection and the research and development of related technologies. Second, it proposes a method for detecting abnormal crowd behavior based on Graph Convolutional Neural Network (GCN). This method first proposes to effectively and accurately locate the abnormal behavior characteristics through the extraction of abnormal behavior characteristics from the auto-encoder of the surveillance video. With the construction of the graph classification model based on SAGPool, the classification of normal and abnormal behavior is carried out. Finally, experimental data show that data displayed with this method meet the expected requirements.
Insulator Defect Detection Based on YOLOv4-tiny with Improved Feature Fusion

Insulator exposed to a wild environment for long-term running usually suffers from the damage of natural calamities that makes them become one of the most fragile components in the whole power system. In this case, even a slight fault will seriously affect the power supply safety of the power grid. Therefore, it is critical to find and identify the faulty insulator efficiently and accurately. However, an insulator is quite a small component in comparison with a huge power system which makes it hard to visible with naked eyes. This characteristic leads to poor recognition accuracy for existing detection methods. In order to solve the above problems, we propose an insulator defect detection algorithm based on an improved YOLOv4-tiny model. It differs from existing insulator detection models, our algorithm applies the effective channel attention network to the feature extraction network of YOLOv4-tiny, which significantly enhances the feature extraction ability of the backbone network. These two-way features of the pyramid can be fully fused in the two-way feature fusion stages seamlessly. Experimental results show that compared with, our proposed algorithm outperforms Faster RCNN and YOLOv3 in average classification accuracy, missed detection, and false detection.
Recommendation Algorithm Based on Object Feature Combination Embedded

With the explosive growth of online learning resources, in order to enable learners to acquire learning materials more accurately, we propose a recommendation algorithm based on object feature combination embedded and knowledge graph. Firstly, the student predictive performance ability model based on Item Response Theory(IRT) was used to identify learners’ abilities reliably, and to model and infer the relevance of knowledge points. Secondly, a recommendation algorithm based on object feature combination embedded and knowledge graph is used to effectively recommend learning materials through multidimensional information of learners and projects. Finally, we propose a modified algorithm for knowledge graph and student ability based on EM algorithm, this algorithm is used to further modify the learner’s ability and the actual difficulty of the test after training, so as to optimize the effect of the next recommendation, which effectively improves the effectiveness of recommendation under sparse data and cold start. According to the analysis of experimental results, our proposed recommendation algorithm can maintain a precision of about 60% in a small training set and maintain a response speed of about 1s to users.
Cross Attention with Transformer for Few-shot Medical Image Segmentation

Few-shot medical segmentation aims at learning to segment a new organ object using only a few annotated support images of the target class, which require the network to have strong generalization performance in extremely few data regimes. There exists a groundbreaking idea of integrating meta-learning methods into few-shot medical image segmentation. However, existing few-shot medical image segmentation methods fail to consider the global anatomy correlation between the support and query set. The information exchange between two branches is too weak to fully carry out a sufficient semantic understanding. This determines how much knowledge the query set learns from the corresponding support set. To alleviate the above-mentioned drawbacks, we propose a novel Cross Attention network based on traditional two-branch methods. Our starting point is to fully integrate the global information of the two parts with the frequently used fusion method in cross-modality tasks. To achieve this goal, we raised two main contributions: (1) The transformer-based Cross Attention module is leveraged to strengthen the fusion of support and query data. and (2) Through the comparable results on two challenging datasets (abdominal segmentation dataset CHAOS and cardiac segmentation dataset MS-CMRSeg), we proved that the traditional meta-learning based methods still have great potential when strengthening the information exchange between two branches. We achieve remarkable improvement in the proposed method compared with currently dominant metric learning-based methods.
Global-Local Feature Alignment Loss for Photorealistic Style Transfer

The problem that needs to be solved for photorealistic style transfer lies in limiting the distortion of texture details of the generated image based on the typical style transfer network. Although the existing methods achieve better stylization results, they lack sufficient style information because they do not consider the feature map comprehensively enough, leading to exposure or artifacts. This article proposes a loss function based on the contrast learning method to constrain the network to extract local and global information effectively. It ensures the consistency of distribution among regional blocks generated based on anchor points and the consistency of comparison between anchor points of the resulting image and content image in their neighborhood. This ensures consistency between local and global information comparisons. To ensure that the network is simple and effective and that enough information is extracted, this article proposes a linear covariance transformation network to achieve faithful stylization by effectively fusing feature first-order statistics with second-order statistics. Experiments show that the proposed method can faithfully achieve realistic stylization and satisfying visual effects.