Reviewerly/exHarmony huggingface dataset:
RIDEN: Neural-based Uniform Density Histogram for Distribution Shift Detection

It is required to detect distribution shift in order to prevent a machine learning model from performance degradation, and human-mediated data analysis from erroneous conclusions. For the purpose of comparing between unknown distributions of high-dimensional data, histograms are suitable density estimators due to its computational efficiency. It is important for histograms for distribution shift detection to have uniform density, which has been demonstrated in existing tree-based or cluster-based histograms. However, existing histograms do not consider generalization capability to out-of-sample data, resulting in degraded detection performance at test time. In this paper, we propose a neural-based histogram for distribution shift detection, which generalizes well to out-of-sample data. The bins of histogram are determined by a model trained to discriminate between a handful reference instances, which reflects their underlying distribution. Due to the batch-wise maximum entropy regularizer calculated from a bootstrap sample, the bins as a subset of the feature space partitioned by the decision boundaries of the model generalize, and thus the histogram keeps its density uniform for out-of-sample data. We evaluate our method on distribution shift detection task using multi-domain real-world datasets. The results show that our method outperforms state-of-the-art histogram-based methods.
Estimation of Bus Passenger Attributes Using Swin Transformer

Human Attribute Recognition is an emerging research topic related to person re-identification in the computer vision and pattern recognition fields. Existing research has mainly focused on the applications of video surveillance and security. In this paper, an algorithm adopting Swin Transformer as the backbone is proposed and verified for bus passenger attribute recognition. To evaluate the performance and effectiveness of the algorithm, a real bus passenger dataset with thousands of images is collected by digital cameras installed on the route bus in Sapporo city. Experimental results on a single passenger image show that our proposed algorithm achieves high accuracy in most attribute categories and proves the feasibility of a practical application. For those attributes with lower accuracy, our experiments reveal the deficiency of the algorithm in tackling passenger images with occlusion, mosaic cover, and viewpoint change. The possibility of enhancing the performance and robustness of attribute recognition by employing continuous passenger images for inference is also highlighted.
A Survey of Low-Light Image Enhancement

Abstract: Image sensors imaging in low-light environments can cause problems such as high image noise, low contrast, and failure to represent large amounts of detailed information, which affect not only human eye observation but also applications related to computer vision and low-light image processing. The use of image enhancement methods and deep learning methods can improve the contrast of low-light images and improve the image quality. The article first introduces the traditional low-illumination image enhancement algorithms categorized and summarizes the improvement process of these algorithms in recent years, then introduces the low-illumination image enhancement methods based on deep learning, and finally introduces the existing low-illumination image datasets and the evaluation criteria of the enhanced images.
Efficient and Accurate Feature Extraction Using Local Density Detector

Feature detection is essential to a large number of vision-based applications. Among the approaches available, keypoint detection-based ones, e.g., SIFT, SURF, and ORB, are very popular. In particular, ORB stands out given its attractive balance of efficiency and efficacy, compared to other methods. However, a major drawback that affects the performance of ORB is the high density of keypoints it detects. In this work, a novel method namely local density enhanced ORB (ORBLD) is proposed. ORBLD mitigates ORB's weakness by adopting a local density detector to regulate the number of the keypoints. This approach achieves lower computational cost and reserves robustness under transformation and environmental changes. ORBLD is evaluated by setting up experiments with a self-driving related dataset, and the results show the reduction of 59.8% of keypoints mainly from redundant area, while the representative keypoints are reserved. ORBLD can facilitate the subsequent steps in feature extraction by optimizing keypoint selection and thus results in overall improved performance.
A Novel Spatiotemporal Attention Convolutional Neural Network for Video Crowd Counting

For most existing crowd counting methods, image-based methods are still used for crowd counting in the presence of video datasets, ignoring powerful time information. Thus, a novel spatiotemporal attention convolutional neural network is proposed to solve the video-based crowd counting problem. Firstly, the first ten layers of VGG-16 are used as the backbone network to extract features, and a single layer of ConvLSTM captures the time correlation of adjacent frames. Then, stacked dilated convolutional layers are used to enlarge the receptive field without increasing the computational load. Finally, a convolutional block attention module is introduced with the adaptive refinement of feature mapping. Its ability to emphasize or suppress information in the channel and spatial dimensions aids information dissemination. Experimental results on the two reference datasets (i.e., Mall and WorldExpo'10) show that the proposed method further improves the accuracy of crowd counting and is superior to the other existing crowd counting methods.
A Joint Model for Text Classification with BERT-BiLSTM and GCN

With the development of Graph Neural Network (GNN), a lot of GNN-based methods have been proposed in text classification. However, GNNs are difficult to capture the word relationship of the context in the document sequence. In addition, these models are common to use one-hot encoding to initialize the node features, which have high dimensions and sparse features. Therefore, it is difficult to represent the correlation between texts. To effectively learn the semantic features of contexts and the association between words, we propose a text classification method based on the BERT-BiLSTM word embedding model and the Graph Convolutional Network (GCN), which is named BBG. The BERT-BiLSTM can learn word embedding representations in different contexts and the semantic relationship of text contexts. The learned representation vectors are provided into GCN as the features of document nodes. The feature representations of the training data and unlabeled test data are learned by performing the label propagation in GCN. Finally, the classification results are output by utilizing the Softmax layer. Experiments on four benchmark datasets show that our method has better performances than existing text classification methods.
Accurate and Efficient Channel pruning via Orthogonal Matching Pursuit

The deeper and wider architectures of recent convolutional neural networks (CNN) are responsible for superior performance in computer vision tasks. However, they also come with an enormous model size and heavy computational cost. Filter pruning (FP) is one of the methods applied to CNNs for compression and acceleration. Various techniques have been recently proposed for filter pruning. We address the limitation of the existing state-of-the-art method and motivate our setup. We develop a novel method for filter selection using sparse approximation of filter weights. We propose an orthogonal matching pursuit (OMP) based algorithm for filter pruning (called FP-OMP). We also propose FP-OMP Search, which address the problem of removal of uniform number of filters from all the layers of a network. FP-OMP Search performs a search over all the layers with a given batch size of filter removal. We evaluate both FP-OMP and FP-OMP Search on benchmark datasets using standard ResNet architectures. Experimental results indicate that FP-OMP Search consistently outperforms the baseline method (LRF) by nearly . We demonstrate both empirically and visually, that FP-OMP Search prunes different number of filters from different layers. Further, timing profile experiments show that FP-OMP improves over the running time of LRF.
An Efficient Image Encryption Algorithm Based on Hyperchaotic and Two Times of Scrambling Diffusion

An image encryption algorithm based on hyperchaotic system is proposed to satisfy the security of image information transmission, the encryption algorithm consists of two times of scrambling and diffusion. The relation between plaintext and key is constructed to generate plaintext related chaotic sequence. Two rounds of scrambling operation are performed to make the pixel positions of the image are sufficiently scrambled, and two-way diffusion is carried out after scrambling, in this way, the value of any one pixel can affect other pixel values as much as possible. The information entropy, histogram distribution, adjacent pixel correlation, plaintext sensitivity, key sensitivity and key space are analyzed. As show by simulation results, our algorithm has good encryption effect and high security.
A Concept Drift Detection Approach Based on Jensen-Shannon Divergence for Network Traffic Classification

Network traffic features change with time and network environment, creating a concept drift problem that leads to a decrease in the accuracy of machine learning-based network traffic classification methods. This is because the traditional network traffic classifiers are static models that cannot adapt to the changes in data distribution. Therefore, we proposed a concept drift detection approach based on Jensen–Shannon divergence, named CDJD. The method uses a double-layer window mechanism to detect changes in data distribution based on the Jensen-Shannon divergence, and thus detects concept drift. After detecting concept drift, the Jensen-Shannon divergence is used to check whether the current concept is a recurrence of the past concept and thus decide whether to reuse the old classifier. The method is experimentally compared with common concept drift detection methods, and the experimental results show that the method can effectively detect concept drift and showing better classification performance.
Object Detection Algorithm Based on Second-Order Pooling Network and Gaussian Mixture Attention

To improve the feature representation ability of the YOLOX algorithm and obtain better detection performance, an object detection algorithm based on second-order pooling network and gaussian mixture attention is proposed. Firstly, the second-order pooling network is added after the PAFPN, and the higher-order statistical information is obtained by calculating the covariance matrix between different channels, to enhance the non-linear modeling capability. Secondly, the mixture attention based on the gaussian function is added after the SPP to model global contexts in the spatial and channel dimensions respectively, which improves the network performance with almost no extra parameters. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 82.6 %, which is 1.6 % higher than the YOLOX algorithm.
A Novel Classification Method Based on Particle Swarm Optimization

Because of the huge amount of traffic generated today, classifying different applications through traffic classification has become a difficult task. However, categorization of network traffic using machine learning suffers from a shortage of tagged traffic data and difficulty in feature selection, as is the case in many applications in the real world. Aiming at solving this problem, this paper proposes a method based on particle swarm optimization (PSO) to do feature selection, combined with machine learning for classification, and the accuracy of classification is used as the fitness of PSO. This experiment uses the ISCXVPN2016 public dataset for experimental study. In addition to the experimental accuracy of 97.71% when the PSO algorithm is used in combination with decision trees, the accuracy of the traditional support vector machines (SVM) algorithm increases by 27.96% when combined with PSO.
Non-intrusive Automatic 3D Gaze Ground-truth System

Driver distraction has surfaced as a significant safety issue worldwide, and the capacity to track a driver's attention via monitoring its gaze direction is one of the most critical features in the modern Driver Monitoring System (DMS). Deep learning based gaze estimation has grown in popularity due to its robustness across operating conditions. Though appropriate network structure design and parameters tuning are important, accurate ground-truth estimation for millions of gaze training images to build the model also plays a critical role in achieving high-quality gaze estimation results. This paper proposes a non-intrusive automatic 3D ground-truth data collection system for large-scale on-bench and in-car data collection, using gamified camera calibration, occlusion invariant mirror-based camera localization, and noise-robust 3D reconstruction algorithms. Experimental results are provided to demonstrate the system's accuracy and robustness even in challenging conditions.
Bone Age Assessment Based on Improved Deep Residual Networks

Bone age plays an important role in the scene of pediatrics and judicial identification, because the traditional bone age assessment is time-consuming, laborious and depends on the experience of physicians, the results of artificial bone age assessment will vary from person to person. This paper has collected the X-ray image from a class A tertiary children's hospital, and presents the convolutional neural network suitable for China 05 bone age assessment. For the collected Chinese 3-16-year-old youth hand bone image, using the stacked denoising autoencoder (SDAE) combined with ResNet50, while reducing the noise of soft tissue and effectively improving the feature extraction ability of the model; Secondly, the 3×3 convolution in the ResNet50 residual block is replaced with a pyramid split attention (PSA) module to get the new model, fusion multi-level features of space and channel attention, adapt to re-define features; Presents the adaptive dual-channel pooling layer by combining the max pooling and average pooling; Use pre-excitement to speed up convergence and label smooth loss function to prevent the model from overfitting, and finally establish a deep learning classification model for China 05 bone age assessment. The experimental results show that the accuracy of ±1 year in this method reaches 93.22% of men, and 91.71% of women. The Mean Absolute Error (MAE) also decreases.
A Differential Privacy K-Means Algorithm for Improving Privacy Budget Allocation

As a privacy protection method with strict mathematical definition, differential privacy has been widely used in various fields of data mining including clustering algorithm. However, the traditional differential privacy k-means algorithm is sensitive to the selection of initial value, and the allocation of privacy budget is relatively single, which reduces the availability of the algorithm. In order to further improve the availability of the differential privacy K-means algorithm, this paper proposes a privacy budget allocation method combining error analysis to optimize algorithm iteration times and merge clustering, and carries out theoretical analysis and experimental verification at the same time. The results show that the algorithm not only satisfies the definition of differential privacy, but also improves the availability of clustering effectively.
A Dynamic Gesture Recognition Method Based on Encoded Video

Most of the video-based dynamic gesture recognition methods require decoding video into raw RGB images. The approved accuracy relies on multiple data patterns, such as depth map or optical flow, in specific scenario. So, the more complexity models, the huger calculation power and storage consumption. In this paper, a new characterized model for spatiotemporal data is proposed to represent the spatiotemporal features of dynamic gestures, take advantage of Intra-frames (I-frame), motion vectors, and residuals in encoded videos, so that the additional consumption of computation and storage caused by decoding videos are escaped. Furthermore, a key predicted frames (P-frame) selection (KPFS) module is proposed to filter those P-frames having no useful information, based on an image entropy estimated with the residuals. The more distinguished features are obtained. Comprehensively experiments are performed on two benchmark datasets, VIVA and SKIG. The results show that our method can achieve an average accuracy of 81.13% and 98.70% using lone RGB data, reduce the storage overhead by 88.5%. The result is similar to that of the state-of-the-art methods with the running speed of more than 4.3 times.
FF-KGAT: Feature Fusion Based Knowledge Graph Attention Network for Recommendation

It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However, existing KG-based models present unique challenges that have the shortcoming of cold start and redundant iterations. To address the issue, we propose a Feature Fusion-based Knowledge Graph Attention Network (FF-KGAT) for the recommendation. FF-KGAT handles the cold-start problem through introducing user characteristics to fully explore the information of users. Additionally, FF-KGAT introduces the feature fusion graph, which removes slight nodes to obtain fewer iterations. Experimental results show that the proposed model significantly outperforms baseline methods. Particularly, the ndcg is increased by 9.83% and 10.25% on two public datasets, compared with the best performance of existing methods.
Voicifier-LN: An Novel Approach to Elevate the Speaker Similarity for General Zero-shot Multi-Speaker TTS

Speeches generated from neural network-based Text-to-Speech (TTS) have been becoming more natural and intelligible. However, the evident dropping performance still exists when synthesizing multi-speaker speeches in zero-shot manner, especially for those from different countries with different accents. To bridge this gap, we propose a novel method, called Voicifier. It firstly operates on high frequency mel-spectrogram bins to approximately remove the content and rhythm. Then Voicifier uses two strategies, from the shallow to the deep mixing, to further destroy the content and rhythm but retain the timbre. Furthermore, for better zero-shot performance, we propose Voice-Pin Layer Normalization (VPLN) which pins down the timbre according with the text feature. During inference, the model is allowed to synthesize high quality and similarity speeches with just around 1 sec target speech audio. Experiments and ablation studies prove that the methods are able to retain more target timbre while abandoning much more of the content and rhythm-related information. To our best knowledge, the methods are found to be universal that is to say it can be applied to most of the existing TTS systems to enhance the ability of cross-speaker synthesis.
Text Classification Based on Graph Convolution Neural Network and Attention Mechanism

Extracting and representing text features is the most important part of text classification. Aiming at the problem of incomplete feature extraction in traditional text classification methods, a text classification model based on graph convolution neural network and attention mechanism is proposed. Firstly, the text is input into BERT (Bi-directional Encoder Representations from Transformers) model to obtain the word vector representation, the context semantic information of the given text is learned by the BiGRU (Bi-directional Gated Recurrent Unit), and the important information is screened by attention mechanism and used as node features. Secondly, the dependency syntax diagram and the corresponding adjacency matrix of the input text are constructed. Thirdly, the GCN (Graph Convolution Neural Network) is used to learn the node features and adjacency matrix. Finally, the obtained text features are input into the classifier for text classification. Experiments on two datasets show that the proposed model achieves a good classification effect, and better accuracy is achieved in comparison with baseline models.
Image Encryption Algorithm Based on Compound Chaotic System and DNA Coding

Aiming at the problems of image encryption, such as poor encryption effect and poor security, a compound chaotic map ILSC is designed with Logistic map and Sine map as seed maps. Based on this, an image encryption algorithm based on compound chaotic system and dynamic DNA coding is proposed. First, according to the DNA random coding rules, the plaintext image is converted into a DNA matrix, and the scrambling operation is performed on it, and then the rows and columns of the DNA matrix are XORed to obtain the ciphertext image. Theoretical analysis and simulation results show that the proposed algorithm has a larger key space, the ciphertext image has higher information entropy, and can effectively resist statistical attacks, brute force attacks and other attack methods, and has better performance.
Model Lightweight Method for Object Detection

The rapid development of object detection technology benefits from the development of convolutional neural network. However, the convolution neural network needs a deep enough convolution layer to obtain more abundant image feature information and the complexity of the network itself, which makes the object detection network have some limitations, such as large amount of model parameters, unable to achieve real-time detection speed, high requirements for computing resources and so on. Based on the efficientdet model and the LD-BiFPN network, this paper explores the difference between the adaptive fusion method and the fast fusion method, and designs a feature layer pruning method of the weight matrix according to the weight matrix representing the importance of the feature layer in the fast fusion method, to prune the LD-BiFPN network to reduce the network parameters. Then convolution filter pruning method is introduced to prune the classification and regression network of the model, so as to reduce the parameters of the network and improve the detection speed. Experiments show that the designed lightweight method can reduce the parameters of the model and improve the detection speed on the premise of ensuring the detection accuracy.t
Joint Multi-Scale Residual and Motion Feature Learning for Action Recognition

For action recognition, two-stream networks consisting of RGB and optical flow has been widely used, showing high recognition accuracy. However, optical flow computation is time-consuming and requires a large amount of storage space, and the recognition efficiency is very low. To alleviate this problem, we propose an Adaptive Multi-Scale Residual (AMSR) module and a Long Short Term Motion Squeeze (LSMS) module, which are inserted into the 2D convolutional neural network to improve the accuracy of action recognition and achieve a balance of accuracy and speed. The AMSR module adaptively fuses multi-scale feature maps to fully utilize the semantic information provided by deep feature maps and the detailed information provided by shallow feature maps. The LSMS module is a learnable lightweight motion feature extractor for learning long-term motion features of adjacent and non-adjacent frames, thus replacing the traditional optical flow and improving the accuracy of action recognition. Experimental results on UCF-101 and HMDB-51 datasets demonstrate that the method proposed in this paper achieves competitive performance compared to state-of-the-art methods with only a small increase in parameters and computational cost.