Reviewerly/exHarmony huggingface dataset:
An Anti-Noise Hybrid Clustering Oversampling Technique for Imbalanced Data Classification

Oversampling techniques have always been favored in the field of imbalanced data classification. Noise processing plays an important role in the field of unbalanced data classification because the noise data directly affects the distribution of newly synthesized samples. We propose a new anti-noise hybrid clustering oversampling technique for imbalanced data classification to synthesize high-quality samples (ANCO). The algorithm is the first to propose a combination of noise filtering and optimization in an oversampling method. Firstly, based on the sample location information, the processing strategy of noise filtering and optimization is designed. Then, sample nearest neighbor interpolation is used to create a new sample. To compare the performance of our approach with representative oversampling, five datasets with variable imbalance rates in KEEL are chosen for testing. The results show that the ANCO algorithm improves the classifier's overall performance.
Based on Coupled Associative Feedback Control of Quantum Satellite Communication Performance Tuning Strategy

Quantum satellite communication has the natural advantages of strong survival reliability and wide coverage, and is currently a research hotspot in the field of communication at home and abroad. The successful launch of the "Mozi" scientific experimental satellite has laid the foundation for the construction of the quantum space-earth integrated communication network. In order to further improve the communication performance of the quantum satellite-ground link, a tuning strategy based on quantum coupled associative feedback control (QCAFC) is proposed in this paper. QCAFC estimates the state information of atoms by measuring the photons leaked in the optical cavity, and adjusts the controller to change the spin state of the atoms in the optical cavity. The evolution of the system is studied, the influence of the QCAFC system on the performance parameters of the satellite-ground quantum communication is analyzed, and the simulation is verified. The simulation results show that in the amplitude damped channel, the QCAFC system can significantly improve the channel capacity and coherence. When transmitting in the plasma environment, when the particle radius is 5, when the transmission distance between the satellite and the earth increases from 50km to 200km, the bit error rate of the system without QCAFC is increased from 7.34×10-3 to 21.93×10-3, and the bit error rate of the system with QCAFC is increased from 4.81×10-3 to 14.72×10-3. Simulation results show that the use of QCAFC system has a significant improvement in the performance of quantum satellite communication.
Multi-granularity Text Semantic Matching Model Based on Knowledge Enhancement

Text matching is a fundamental task in natural language processing. It is widely used in information retrieval, text excavation and other fields. There are many problems, such as multiple meanings of words and improper word segmentation in text. These problems lead to the contextual information of sentences and the implied semantic information not being extracted effectively. Therefore, we propose a Multi-granularity Knowledge Enhancement (MGKE) model. Firstly, we perform text embedding at character and word granularity. At the same time, the HowNet external knowledge base is used to solve the problem of multiple meanings of a word. Secondly, we introduce an attention mechanism to capture the hidden information at both character and word granularity, which enhances the semantic information of the sentences. Finally, we use pooling to extract global and critical information from the sentences to predict the semantic similarity of the sentence pairs. We do some experiments on the LCQMC and BQ datasets. The results show that the model effectively improves the accuracy of short text semantic matching.
CBAM-DCE: A Non-Reference Image Correction Algorithm for Uneven Illumination

Affected by the change in daytime illumination sequence and by the shooting angle in the complex field environment, the kiwifruit images possess the unfriendly features of uneven illumination, such as local darkness and local brightness. The ill-posed image with uneven illumination will seriously constraint the subsequent image analysis processing. Current deep learning methods have achieved satisfactory results, and a large number of paired images (one is the input image, one is the ground truth image) is required to train the better network performance. However, it is difficult to capture ground truth images of the kiwifruit in the field. Based on this, the paper proposed Convolutional Block Attention Module Deep Curve Estimation (CBAM-DCE) to accomplish a non-reference illumination unevenness correction for field kiwifruit images. A deep learning network model is used to estimate the image-specific curve for image enhancement, and a non-reference loss function is applied to evaluate the image enhancement effect. Compared with seven related enhancement algorithms, the presented algorithm shakes off uneven illumination or normal-light image pairs for training. Five different public datasets and our Kiwifruit dataset were used in the experiments. Experiments demonstrate that our proposed CBAM-DCE is superior to other state-of-the-art algorithms for enhancing natural images under different lighting conditions.
A Dual-Task Deep Neural Network for Scene and Action Recognition Based on 3D SENet and 3D SEResNet

Aiming at the problem that scene information will become noise and cause interference in the feature extraction stage of action recognition, a dual-task deep neural network model for scene and action recognition is proposed. The model first uses a convolutional layer and max pooling layer as shared layers to extract low-dimensional features, then uses 3D SEResNet for action recognition and 3D SENet for scene recognition, and finally outputs their respective results. In addition, to solve the problem that the existing public dataset is not associated with the scene, a scene and action dataset (SAAD) for recognition is built by ourselves. Experimental results show that our method performs better than other methods on SAAD dataset.
GA-SATIC: Semi-autoregressive Transformer Image Captioning Based on Geometric Attention

Image captioning is a cross-domain task involving image and natural language processing. Most of the current models follow an encoder-decoder architecture, where the encoder takes image feature vectors as input, and the decoder uses an autoregressive or non-autoregressive approach for decoding. However, most models extract the feature vectors of an image through region proposals of the object detector, without considering the relative spatial relationship between objects. When decoding, the autoregressive decoding method is adopted, that is, the next word is generated based on the generated word, and the generation is performed step by step, which will lead to high delay in the inference process. To solve this problem, scholars have proposed a non-autoregressive approach, which speeds up inference by generating all words in parallel, but reduces the quality of generated captioning. Aiming at the above problems, this paper proposes a semi-autoregressive Transformer model based on geometric attention. The encoder integrates the relative spatial relationship between the detected objects through geometric attention and appearance attention, so as to enhance spatial awareness; the decoder adopts a semi-autoregressive decoding method, which is serialized globally and paralleled locally, enabling the model to achieve a better trade-off between decoding speed and captioning accuracy. Extensive experiments and ablation studies on the MSCOCO dataset have shown that the model achieves better performance compared to state-of-the-art models.
NMT Sentence Granularity Similarity Calculation Method Based on Improved Cosine Distance

Aiming at the problem of semantic lack of sentence similarity calculation in the process of metamorphosis test of neural machine translation system, an NMT sentence granularity similarity calculation method based on improved Cosine Distance is proposed. Text vectors are constructed through the improved TF-IDF weights, and the combination of Edit Distance and Jaccard similarity coefficient is used as a suppressor for cosine similarity. Experiments on neural machine translation systems such as Alibaba Translation and Baidu Translation on the UM-Corpus dataset show that, compared with the method based on Edit Distance, this method improves the Pearson correlation coefficient and Spearman correlation coefficient of the reference translation method by 20.5% and 12%, respectively. And this method is closer to the BLEU and METEOR evaluation results based on the reference translation, the evaluation accuracy is higher.
SUMSUG: Augmented Abstractive Text Summarization Model with Semantic Understanding Graphs

As a research hotspot in natural language processing, automatic text summarization has been greatly developed. A summary should be a generalization based on depth understanding of the text. However, the existing models should not understand the semantic information of the text, so the generated summaries deviate from the semantics of the text and have low accuracy. This paper proposes an Augmented Abstractive Text Summarization Model with Semantic Understanding Graphs (SUMSUG). The model uses dual encoders, a text encoder and a graph encoder to guide the generation of summaries. And it obtains the features of the context from the text encoder and the structure features from the graph encoder. By fusing them the model obtains fuller semantic information. We evaluate the model on the Gigaword dataset. The experimental results show that the model performs better than other models, which prove the effectiveness of our model.
Few-Shot Image Classification Based on Cross-Dimensional Interactive Attention

In recent years, deep learning techniques have achieved great success in traditional image classification tasks, however, it is often difficult to achieve good results with a small amount of labeled data and prone to overfitting. Therefore, scholars have started to focus on image classification methods based on few-shot learning. The prototype network uses the mean value of the support set samples as the prototype, and achieves classification by calculating the distance between the query set samples and the prototype. To enhance the feature representation capability of the prototype network, this paper proposes a few-shot image classification method based on cross-dimensional interactive attention. The algorithm uses the pre-trained model Resnet-12 to extract deep features of images and introduces the cross-dimensional interactive attention mechanism to link the information between channel and spatial dimensions through triple attention, which enhances the information interaction of each dimension. Meanwhile, in order to improve the problem of insufficient generalization ability of the prototype network, this algorithm uses a gradient-centered optimization algorithm to zero-mean the weight gradient, which improves the generalization ability of the network and improves the classification accuracy. Extensive experimental results show that the proposed algorithm performs well in the few-shot image classification task.
Prompt and Contrastive Learning for Few-shot Sentiment Classification

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings.
Maximum Entropy Multi-threshold Recursive Algorithm for Circular Histograms Based on Dynamic Programming

Maximum entropy thresholding on circular histogram is a color image thresholding method, which makes full use of the color information of the hue H component in the image HSI color space and can effectively separate the target from the background. However, there are problems of large computational effort and long computation time in the multi-threshold case. In this paper, dynamic programming is used to optimize the numerical computation in the segmentation process of the multi-threshold maximum entropy method, and the circular histogram maximum entropy multi-threshold method based on dynamic programming is given. Experimental comparison with the circular histogram maximum entropy recursive algorithm shows that the performance of the two fast algorithms is comparable at double thresholding; however, the dynamic programming-based maximum entropy on circle thresholding method is faster than the recursive-based maximum entropy on circle thresholding method when the number of thresholds is greater than 2.
Research on Image Description Generation Method Based on G-AoANet

Most of the image description generation methods in the attention-based encoder-decoder framework extract local features from images. Despite the relatively high semantic level of local features, it still has two problems to be solved, one is object loss, where some important objects may be lost when generating image descriptions, and the other is prediction error, as an object may be identified in the wrong class. In this paper, a G-AoANet model is proposed to solve the above problems. The model uses an attention mechanism to combine global features with local features. In this way, our model can selectively focus on both object and contextual information, improving the quality of the generated descriptions. Experimental results show that the model improves the initially reported best CIDEr-D and SPICE scores on the MS COCO dataset by 9.3% and 5.1% respectively.
Efficient Vector Store System for Python using Shared Memory

Many e-commerce companies use machine learning to make customer experience better. Even within a single company, there will be generally many independent services running, each specializing in some aspect of customer experience. Since machine learning models work on abstract vectors representing users and/or items, each such service needs a way to store these vectors. A common approach nowadays is to save them in in-memory caches like Memcached. As these caches run in their own processes, and Machine Learning services generally run as Python services, there is a communication overhead involved for each request that ML service serves. One can reduce this overhead by directly storing these vectors in a Python dictionary within the service. To support concurrency and scale, a single node runs multiple instances of the same service. Thus, we also want to avoid duplicating these vectors across multiple processes.
Lightweight Improved Based on YOLOv4 Object Detection Algorithm

To address the problem that the existing object detection network models are large in size and complex in operation and cannot satisfy both detection speed and accuracy under the limited resources and small size platform. Based on YOLOv4 as the benchmark network, a lightweight object detection model LW-YOLO is proposed. Firstly, the backbone feature extraction network is replaced with MobileNetv1, while the number of feature fusion network parameters is significantly reduced by the depth separable convolutional module. Then the BN layer coefficients are used as scaling factors for the importance of the convolutional channels, the scaling factors are sparse using polarization regularization, the errors before and after pruning are reconstructed using least squares and channel weighting methods. The appropriate pruning thresholds are obtained by minimizing the reconstructed errors, the channels with small scaling factor values are eliminated to achieve the lightweight. The experimental results on the VOC (Visual Object Classes) dataset show that the detection accuracy of LW-YOLO is 87.00%, and the FPS(Frames Per Second ) reaches 48.89, which is better than the original YOLOv4 algorithm. It also significantly reduces the number of parameters, computation, and model size, which is more suitable for application in resource-poor embedded mobile devices.
Integrating Hybrid Features for Document-Level Event Role Extraction Method

Event extraction is a sub-task of information extraction and is an important part of natural language processing. Depending on the range of features used, event extraction methods are classified as sentence-level or document-level. However, document-level event extraction is more practical for practical tasks. Document-level event extraction is a difficult task, as it requires features to be extracted from a larger amount of text to determine which span of text is the desired event element. However, most methods do not utilize both sentence-level and document-level features. In order to utilize hybrid feature information and fuse it, this paper proposes a document-level event extraction method that integrating hybrid features. The event extraction method is based on Dynamic Multi-Pooling Convolutional Neural Network (DMCNN) and Bi-directional Long Short-Term Memory (BiLSTM), combined with self-attention mechanisms and Conditional Random Field (CRF). We evaluate the model proposed in this paper on the MUC-4 dataset and the experimental results show that our proposed model outperforms previous work.
UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism

To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.
Multi-instance learning anomaly event detection based on Transformer

Multi-instance learning (MIL) is the dominant approach for weakly supervised anomaly detection in surveillance videos. The shortcomings of using the features extracted by networks such as Convolutional 3D (C3D) or inflated 3D-ConvNet (I3D) alone to extract video context features have prompted the emergence of various abnormal event detection algorithms based on attention mechanisms. Vision Transformer (ViT) applies transformer to the field of computer vision for the first time and demonstrates its superior performance. In this paper, we propose a multi-instance learning anomaly event detection method based on Transformer, called MIL-ViT, which uses an inflated I3D pre-training model to extract Spatio-temporal features, and then inputs features into the ViT encoder to extract the particular salient pieces of information, and the anomaly scores are obtained. Furthermore, we introduce the MIL ranking loss and the center loss function for better training. The experimental results on two benchmark datasets (i.e. ShanghaiTech and UCF-Crime) show that the AUC value of our method is significantly improved compared with several state-of-the-art methods in recent years.
Image Thresholding Segmentation Algorithm Based on Two-parameter Cumulative Residual Masi Entropy

In order to solve the problem that the thresholding algorithm based on Masi entropy is not effective in segmenting images with non-obvious grayscale changes, an image thresholding algorithm based on two-parameter cumulative residual Masi entropy is proposed in this paper. Firstly, the two-parameter Masi entropy formula is obtained by generalizing the classical Masi entropy with two adjustable parameters, and the flexibility of parameter selection improves the adaptability of the proposed algorithm to images; secondly, the concept of cumulative residual type entropy is used to propose a two-parameter cumulative residual Masi entropy formula to overcome the negative effect of non-obvious grayscale changes on image segmentation; finally, the two-parameter cumulative residual Masi entropy is used to construct the objective function, and the image segmentation is realized by maximizing the objective function. Experiments are conducted for nondestructive detection images and natural images, and the results show the effectiveness of the proposed algorithm.
Maneuvering Targets Tracking Method with Unknown Parameters based on Gaussian Mixture Probability Hypothesis Density

With the target velocity unknown or inaccurate, Gaussian mixture probability hypothesis density (GM-PHD) filter is easy to make false estimation or wrong tracking of the target, especially for maneuvering targets. For the unknown velocity singer maneuver model, this paper proposes a velocity estimation method, which can quickly calculate the velocity information from the position state information obtained by the GM-PHD filter, and then transfer it to the 5-point median and 3-point linear combination smoothing to form the interference cancellation velocity. Finally, the threshold value is set through experience to decide whether to feed back the velocity information to the singer maneuver model. This method can improve the target tracking performance of the maneuvering model with unknown velocity. Simulation results verify the effectiveness of this method.
Visual Correlation Filter Tracking for UAV Based on Temporal and Spatial Regularization with Boolean Maps

Object tracking is now widely used in sports event broadcasting, security surveillance, and human-computer interaction. It is a challenging task for tracking on unmanned aerial vehicle (UAV) datasets due to many factors such as illumination change, appearance modification, occlusion, motion blur and so on. To solve the problem, a visual correlation filter tracking algorithm based on temporal and spatial regularization is proposed. It employs boolean maps to obtain visual attention, and fuses different features such as color names (CN), histogram of oriented gradient (HOG) and Gray features to enhance the visual representation. New object occlusion judgment method and model update strategy are put forward to make the tracker more robust. The proposed algorithm is compared with other six trackers in terms of distant precision and success rate on UAV123. And the experimental results show that it achieves more stable and robust tracking performance.