Reviewerly/exHarmony huggingface dataset:
Fine-grained Image Classification Based on Knowledge Distillation

Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model’s generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security.
Summarizing Videos using the Uniqueness of Video Frames that Mask Adjacent Frame Information

Video summarization can help people retrieve videos quickly. Existing unsupervised video summarization models make insufficient use of video frame uniqueness in the self-attention mechanism and have redundancy in the computation of uniqueness. In this paper, we rethink the relationship between attentive uniqueness and diversity of video frames, improve the calculation method of video frame uniqueness by masking adjacent frame information in the model framework based on a self-attention mechanism, and simplify the concentrated attention mechanism with it. In addition, we propose a new loss function called Deviation loss, which uses the uniqueness of video frames to calculate and helps train video summarization models to obtain diverse representations. The experimental results show that the proposed method outperforms other state-of-the-art unsupervised summarization approaches on the SumMe dataset, and also demonstrates competitiveness on the TVSum dataset.
MSR: An Efficient Model for Personalized Movie Recommendation Based on Attention and Gating Mechanism

Personalized recommendation is an important technical means to deal with information overload and meets users’ personalized customization needs. In response to the insufficient extraction of user historical features and cold start problem in traditional movie recommendation systems, we introduce an effective personalized movie recommendation model named Movie join with Starring and Ratings (MSR), based on attention and gate mechanisms. The paper uses multi-head attention mechanism to capture the intrinsic relationship between various data fields in user viewing records and obtains user features through the Basic Information-Rating Joint Attention Network (BRJA); the gate mechanism is used to integrate the basic information and reputation rating of the movie into the movie representation vector, obtaining candidate movie features. The model can provide good recommendations even with limited user information, effectively addressing the cold start problem. Comparative experiments on the public dataset Movie Lens and ablation experiments on key modules have demonstrated the effectiveness of the MSR model.
Improved Kernel-based Fuzzy Clustering Algorithm Based on Maximum Tsallis Entropy

This paper proposes an improved kernel-based fuzzy clustering algorithm based on maximum Tsallis entropy (MTE-IKFC). The algorithm is on the basis of the kernel-based fuzzy clustering algorithm to reduce the influence of data that doesn’t belong to the current cluster on the iterative solution of the corresponding clustering center, and suppress the convergence of different clustering centers when the regularization coefficient is small. In order to effectively reduce the influence of differences in sample distribution on the solution of clustering center, the weight based on relative density is introduced to make the clustering results more reasonable. Finally, Shannon entropy is extended to Tsallis entropy with a generalization parameter q, which further improves the performance of the algorithm. In the experimental part, compared with the other four clustering algorithms, the proposed algorithm shows superior clustering performance and the best robustness.
Automatic Segmentation of Rectal Tumor on Magnetic Resonance Images via a Deep Discriminative Model Consisting of Asymmetric U-Net and Convolutional Conditional Random Field based Post-processing

The process of manual delineating is frequently time-consuming and can result in low consistency. Our goal was to design a deep discriminative model (DDM) to mitigate these issues of magnetic resonance imaging (MRI) for rectal tumor lesion boundary delineation, and we proposed an asymmetric U-Net rectal tumor MR images segmentation algorithm based on multi-scale dilated convolutional inputs and convolutional conditional random fields (ConvCRFs). A multiscale convolutional input module was designed as a preprocessing step to enrich the extraction and input of global contextual semantic information; then, an asymmetric U-Net network combined with ConvCRFs was used to discriminatively fine-tune the segmentation results so as to enhance segmentation accuracy of tumors. To verify the feasibility of the algorithm, the experiments were conducted on a private dataset provided by a medical institution, and the experimental Dice coefficient reached 0.876 on T2WI images, 0.851 on DWI images, respectively, indicating the existence of an important clinical guidance value for rectal tumor image segmentation algorithms.
An Overlapping NAM Segmentation Approach for Grayscale Images and Its Applications to Moment Calculation

Moments are widely used in image classification, pattern recognition and scene analysis because of their translation, rotation and scaling invariance, so it is of great theoretical importance and application to study moment calculation. According to the fast moment calculation formula derived from integration, the moment value of a binary image can be calculated in O(N) time as long as the coordinates of the upper left and lower right vertices of each sub-pattern after its division can be obtained, where N is the number of sub-patterns. In recent years, many excellent algorithms have emerged to transition from the study of moment computation of binary images to the study of moment computation of grey images, including the moment computation algorithm based on the image block representation (IBR) block scan and the moment computation algorithm based on non-symmetry and anti-packing model (NAM) block scan, etc. In essence, all these algorithms try to reduce the number of subpatterns to be divided as low as possible through the greedy algorithm, so as to reduce the time consuming moment computation. Since NAM is a raster scanning method and can approximate the optimal result and reduce the number of subpatterns significantly, and the overlapping NAM block scanning method (ORNAMC) can further reduce the number of subpatterns. In this paper, we proposed an ORNAMC-based fast moment calculation algorithm, which can theoretically and experimentally improve the efficiency of moment calculation.
Optimized Coding Methods of the Rectangular Non-Symmetry and Anti-packing Model for Gray Images

Inspired by the idea of rectangular optimized coding scheme for binary images, this paper proposes three optimized coding methods aimed at gray image compression using the rectangular non-symmetry and anti-packing model (RNAMC). The adaptive arithmetic encoding and the adjacent position correlation encoding based on column bisections are used to improve representation efficiency of gray images. The experimental results show the effectiveness of the three optimized coding methods proposed in this paper. These methods can improve the compression ratio in the RNAMC algorithm and have a good application prospect in some commercial applications related to image processing, such as reducing storage space, increasing processing speed, and improving pattern matching efficiency.
Trans-DAE: Transformer-based Double Autoencoder for Anomaly Detection on Attributed Networks

Anomaly node detection on attribute networks has recently attracted increasing research attention and has a wide range of applications in many fields, such as cyber security, finance, and healthcare. However, most existing approaches ignore the complex cross-modal interactions between network structure and node attributes. In this paper, we propose a Transformer-based dual autoencoder framework (Trans-DAE) for anomaly detection, which captures the complex interactions between the two modalities to improve the performance of subsequent detection tasks. Precisely, Trans-DAE consists of a structural self-encoder and an attribute self-encoder. In addition, a graph attention layer (GAT) is used in the structural self-encoder to learn the importance between nodes and neighboring nodes to obtain high-quality node embeddings. In addition, cross-modal interactions between structures and attributes are captured using node embeddings and attribute embeddings as input to the Transformer layer. Finally, anomalous nodes are detected by analyzing the reconstruction errors of structures and attributes. Extensive experiments on real data demonstrate the effectiveness of the method.
Color Image Representation Based on Overlapping Rectangular Subpatterns

Image representation has an important role in the fields of computer vision, artificial intelligence and medical imaging. In this paper, based on the research of the existing image representation algorithms, a novel image representation method based on the overlapping non-symmetry and anti-packing pattern representation model (ONAM) for color images is proposed and applied to image segmentation and image compression with the help of the bit-plane decomposition method. The innovation of this paper lies in the following aspects. First, a new overlapping NAM image representation algorithm based on the idea of overlapping rectangular division of binary images is combined and its concrete implementation steps are given. Second, unlike the previous ONAM algorithm based on multiple coding matrices, the overlapping subpattern NAM algorithm proposed in this paper uses only one auxiliary matrix for coding and put forward three criteria on the overlapping of homogeneous blocks. Third, with the help of bit-plane decomposition method, this paper extends the overlapping NAM to a color image representation algorithm. The experimental results presented in this paper demonstrate that our proposed ONAM representation of color images is able to achieve high representation efficiency and that it outperforms the popular hierarchical representation methods.
Hybrid Resource Scheduling Model for Data Intensive Computing

In a large-scale cluster or cloud computing environment, the execution of data-intensive operations will cause a large amount of redundant energy consumption. In order to improve computing energy efficiency and ensure the service quality of users’ operations, the computing resources are first divided into two categories: recyclable and non-recyclable, and a hybrid resource scheduling model is proposed. The two-layer scheduling strategy combining coarse and fine-grained size is adopted to realize coarse-grained energy quota scheduling in the upper layer through a periodic prediction model, so as to improve the utilization rate of computing energy efficiency; at the bottom level, the scheduling of virtual machines according to the scheduling signal and load information of energy quotas is sorted by weights. The simulation experiment results show that compared with other models, the scheduling performance has achieved a good optimization effect, the maximum operation completion time is 4.3%, and the cluster node power consumption is 5.1%.
Social distance calculation based on improved Yolov7

In daily life, maintaining proper social distance can effectively stop the spread of epidemic viruses and help protect our lives and property. In this study, we developed a social distance detection system designed by a promoted yolov7 deep neural network, and ensured that the system distance error is fully applicable to daily pedestrian distance detection. Based on the faster and more accurate yolov7 framework, we first use a self-made specific dataset for data augmentation, then use k-means++ to design a pre-selected box that fits the pedestrian size, finally, use the SIOU loss function to replace CIOU to obtain faster convergence speed and optimization effect. The experimental results show that the detection speed of the network is 106 FPS and the accuracy value is 93.1%, which is 0.9% better than the original algorithm and achieves the effect of real-time pedestrian distance detection. Studying the imaging principle of monocular camera, we use Zhang’s calibration method to obtain the internal reference and aberration parameters of the camera, then use the algorithm of inverse perspective change to transform the region of interest into the viewpoint of the bird’s eye view, applying the scale factor in the viewpoint of the bird’s eye view can be converted into the specific distance of pedestrians in the actual three-dimensional space. The experiment was conducted in the entrance of the liberal arts building, and the calculated error was kept within 20mm, which ensured the availability of safe social distance.
A Convolutional Computing Design Using Pulsating Arrays

The training and reasoning of convolutional neural networks (CNN) require a large amount of computation, among which the most core operation is convolutional operation. In order to accelerate convolutional operation, many methods for accelerating convolution have emerged in recent years, including sliding window method, Winograd algorithm, etc. Although the sliding window method and Winograd algorithm can improve the computational efficiency of convolution to a certain extent, they also have some drawbacks. The sliding window method requires a large amount of data reuse and transmission, thus requiring high bandwidth and low latency memory and data paths, which is also the main challenge of its hardware implementation; The Winograd algorithm requires preprocessing of convolutional kernels and input data, which increases the computational and storage burden in the early stage. At the same time, for convolutional kernels of different sizes, matrix transformations need to be regenerated, which increases the difficulty of algorithm implementation. Based on this, this article adopts a pulsating array convolution calculation method based on fixed weights.
Iterative weakly supervised semantic segmentation network with fused superpixel clues

Most weakly supervised semantic segmentation networks lack semantic information between pixels, resulting in incomplete segmentation of targets. In this paper, we propose an iterative weakly supervised semantic segmentation network with fused superpixel clues. First, we generate attention class activation maps from the intermediate layers of the classification network, then use superpixel clues to optimize the class attention region and generate pseudo-labels with pixel correlations. After training the semantic segmentation network on these pseudo-labels, we continue to input the predicted pixel map iteratively into the superpixel network and the semantic segmentation network until the entire process is complete and the final semantic segmentation map is obtained. Results on PASCAL VOC 2012 demonstrate that the proposed method has superior segmentation performance compared with other weakly supervised semantic segmentation methods.
Multi-scale Super-resolution Image Reconstruction based on Visual Feature and Scale Feature Transformation

Aiming at the shortcomings of fixed scale and neglecting human visual characteristics of the existing deep learning-based super-resolution image reconstruction (SRIR) methods, this paper proposes a multi-scale SRIR model based on the visual feature and the scale feature transformation (SFT). Different from the traditional methods, the proposed model takes the scale factor as input parameter, and obtains the corresponding deep features by applying scale extension and SFT to the backbone network. Meanwhile a weight prediction module is constructed to dynamically learn the multi-scale up-sampling filters. By multiplying them with the deep features, the images with different super-resolutions are accordingly reconstructed. To further improve the visual effect of the reconstructed images, a frequency division-based loss function is constructed which matches the human visual perception well. Experimental results on several datasets such as Set14, Urban100 and General100 indicate that the proposed model can not only obtain super-resolution images at different scales, but also make the reconstructed image have more clear details and better visual effect.
Shadow Removal from a Single Image Using an Enhanced Generative Adversarial Network

Image shadow removal is challenging due to the diversity of shadows and the dependence on the background of the area covered by shadows. In this paper, we design an enhanced generative adversarial network (EGAN) to handle the shadow removal task. EGAN is a complicated generative adversarial network that may exploit previous info, like shadow masks and lightness estimation, for improving recovery capability. In addition, EGAN can effectively utilize the details of shadow regions and eliminate artifacts. EGAN owns three main advantages. First, EGAN employs stricter rules for discriminators, which can upgrade the instability of traditional GAN. Second, EGAN involves a completely unique enhanced attention module, which may efficiently pick up different types of previous info to accelerate the learning efficiency of image reconstruction. Finally, EGAN is essentially an original cascaded network, that is, DRDU, which is beneficial for efficiently detecting hierarchical features of image recovery. Simulation experiments are conducted to verify the effectiveness of the proposed method on two benchmark datasets including SRD and ISTD, the strengths of our proposed method are indicated over the current representative ones according to subjective performance and objective estimation indexes.
Doctor recommendations based on the ERNIE model

In order to recommend appropriate departments and doctors to patients, this paper uses the ERNIE pre-trained language learning model to classify corresponding departments for patient’s disease descriptions, then calculates the similarity between patient’s disease descriptions and doctors’ expertise, so as to show patients the ranking of doctors in corresponding departments for patients to choose consultation. As is shown in the experimental results on Haodf.com datasets, the ERNIE model outperforms other models in terms of evaluation indexes, and the accuracy rate is improved by 3.54%-11.51%.
Research on UAV Small Target Detection Algorithm Based on Improved YOLOv5s

A UAV small target detection algorithm based on improved YOLOv5s is proposed to address the problems of missed detection and low accuracy in UAV aerial photography. Firstly, to solve the problem of missed detection in small target detection, the GAM attention mechanism is added to the backbone network to expand the detection range and improve the detection capability. Secondly, to solve the problem of easy semantic loss in small target detection, CAM module is added, which can effectively enrich the semantic information. The experimental results show that the average detection accuracy (mAP) of this algorithm reaches 37.88% on the VisDrone2019-DET dataset, which is 5.35% better than YOLOv5s.
Micro-expression Recognition Based on Apex Frame Using Deep Learning

Emotions reflect human’s inner feelings. The precise recognition of emotions is the key to social communication and human-computer interaction. Micro-expression (ME) cannot be completely suppressed, and it can reveal human’s real emotional state. Therefore, it is an important cue for emotion recognition. However, due to the rapid and subtle nature of ME, extracting efficient features from ME sequences is tricky. ME recognition is still a challenging problem. Recently, deep learning based methods have been proposed in the field of pattern recognition and have achieved promising recognition performance. In this paper, we propose a ME recognition method based on apex frame using deep learning models to extract deep features and perform recognition. Firstly, we introduce deep learning based face detection model, named RetinaFace, to perform face detection and pre-processing operations on apex frames. Secondly, due to the limited availability and imbalance of ME data, we propose to use Deep Convolutional Neural Networks (DCNNs) and transfer learning for ME features extraction and recognition. By introducing a DCNNs based face recognition model called FaceNet, this study transfer the prior and learned knowledge in the field of face recognition to the field of ME recognition, thus reducing the training process and improving the performance of ME recognition. Experiments conducted on CASME II prove that our proposed framework is feasible and achieves good recognition performance. Furthermore, we investigate the impact of the number of apex frames on ME recognition, providing clues to address the problem of limited samples in deep learning-based ME recognition. Our work complements deep learning-based methods for emotion recognition and ME recognition, and demonstrates the powerful capabilities and promising potentialities of deep learning frameworks in ME recognition.
Design and Development of Posture Detection System Based on YOLO - OpenPose

With the development of machine vision and multimedia technology, posture detection and other related algorithms have been widely used in the field of human posture recognition. Traditional video surveillance methods have the disadvantages of slow detection speed, low accuracy, interference from occlusion, and poor real-time performance. This paper proposes a posture detection system based on YOLO-OpenPose. Based on the YOLO network, a corresponding human recognition dataset is created for complex scenes. OpenPose is used to detect human key points, and Kalman filtering multiobject tracking method is applied to predict the target state of human objects in occluded areas. Real-time detection of human postures (standing, sitting, walking, falling, and lying down) is achieved with corresponding alarms to ensure the timely detection and processing of emergencies.
Research on Spam Review Detection: A Survey

With the vigorous development of e-commerce, enterprises and individuals generally use network platforms to obtain information and conduct commodity transactions, providing convenience, speed and efficiency to the society and people, but also accompanied by endless fake transactions, deceptive spam reviews, and credit fraud. Online stores improve their system rankings through deceptive spam reviews and other methods to obtain profits. To solve this problem, spam review detection becomes one of the key technologies. This paper summarizes the existing research on spam review and spammer detection, and classifies spam review and spammer detection technologies to methods based on review content features, reviewer behavior characteristics, reviewer graphs and deep learning. Among them, deep learning-based methods for detecting spam reviews and spammers mainly focus on the cold start problem and deep representation of reviews and reviewers. Finally, the paper summarizes the research problems and future directions.