Reviewerly/exHarmony huggingface dataset:
Multi-Object Tracking with Spatial-Temporal Correlation Memory Networks

Resistance to object appearance deformation and local occlusion is still one of the challenges of multi-object tracking algorithms. Most popular algorithms rely on time-consuming numerical optimization and complex manual design strategies to integrate object appearance information and motion information, so as to alleviate the adverse effects of object appearance deformation and local occlusion on the trajectory updating. This paper proposes a Spatial-Temporal Correlation Memory (STCM) module which can adaptively aggregate useful information from rich historical information in memory. By mining the time dimension information, the STCM module can guide the backbone network to extract the current frame effectively, and adapt to the change in the object’s appearance in the tracking process. Specifically, the STCM module can record the foreground-background information in the history frames and direct the backbone network to focus on the useful information in the current frame. Experiments on the MOT17 data set show that our method outperforms the baseline method and current advanced method in index MOTA and IDFI.
Panoramic Depth Estimation Algorithm Based on Attention Module

With the continuous progress and development of virtual reality technology, the panoramic image is more and more widely used. The refinement of the panoramic image requires clear and accurate depth map information. Traditional monocular panoramic depth estimation methods can not obtain accurate depth information due to the lack of available information on the original color image. However, the existing depth estimation methods based on deep learning are mostly for conventional planar images, and it is difficult to generate high-precision depth maps for detailed features of panoramic images. To solve this problem, we propose a panoramic depth estimation algorithm based on the attention module in this paper. This algorithm adds an attention module to the feature extraction convolution layer to extract salient features of panoramic images. Meanwhile, a joint loss function is designed to optimize the network according to the panoramic image depth estimation task. Experiments show that the method proposed in this paper effectively solves the untargeted detail extraction in the network training process and improves the image quality of the generated panoramic depth map.
Segmenting against Dense Distribution and Occlusion in Road Scenarios

Instance segmentation in road scenarios is more difficult because of the dense distribution of objects and severe occlusion among pedestrians and vehicles. To tackle the aforementioned issues, we take the popular single-stage instance segmentation framework CondInst as the baseline model and make improvements from the following three aspects: (1) introducing semantic and positional information into the mask head by concatenating the features from classification and regression head; (2) paying more attention to densely distributed objects by adopting a compactness loss in the segmentation task and increasing the loss weight of their samples; (3) proposing a novel strategy to select positive samples and depicting their qualities as the proportion of instance mask to the corresponding grid. We conduct comprehensive experiments on challenging datasets Cityscapes and BDD 100K to demonstrate the effectiveness of our proposed methods, and better results are obtained.
Channel Interaction Attention Networks for Fine-Grained Visual Classification

The high similarity between subcategories and low similarity within subcategories in fine-grained visual classification leads to difficulties in classification. Most existing methods take a single image into a model and perform image classification by locating discriminative regions and learning fine-grained features, ignoring the contrast information between images which also facilitates classification. A method named CIANet was proposed, which includes: (1) channel interaction structure, using the bilinear operation to obtain channel correlation between images, and integrating original features to extract complementary features. (2) The attention boosting and suppression module takes the channel features with the highest weight as the attention map for boosting features and suppressing significant regions of the image, guiding the model to learn more fine-grained differentiating features. CIA-Net does not rely on extra bounding boxes and partial annotations for end-to-end training. Experiments were conducted on three benchmark datasets (CUB-200-2011, FGVC-Aircraft, and Stanford Cars) and the results showed that CIA-Net has higher classification accuracy.
A Novel Graph-Based Structural Dissimilarity Measure for Video Summarization

Effective detection of shot boundaries is important for video summarization methods based on shot boundary detection. However, various gradual shot boundaries (such as, fade in, fade out, dissolve) pose a great challenge to shot boundary detection. Previous work constructs graph models based on feature histograms from images and analyzes the structural changes of graphs, thus improving the detection of gradual shots. In this paper, we develop a new quantization method to calculate the structural change of the graph so as to more accurately locate the gradual shot boundaries. Statistical analysis methods are performed to analyze the data to be detected with past data to achieve real-time shot boundary detection. Experimental results on the VSUMM dataset show that our method outperforms some state-of-the-art methods on the F-Score.
Target detection on lightweight device based on Compressed YOLOv5s6

In recent years, with the development of deep learning and target detection, the accuracy of detection network is higher and higher, and the increase of network parameters and the decrease of inference speed. However, in actual application scenarios, the detection network needs to be deployed on some mobile or lightweight devices. To solve this problem, this paper proposes a method to compress the model. Based on YOLOv5s6 model, the channels with small weight are removed through sparse training and channel pruning, and then fix the model accuracy by knowledge distillation. Finally, the lightweight model Compressed YOLOv5s6 is obtained. The experimental result shows that the Compressed YOLOv5s6 model reduces 95.1% of the parameters, 30% of the inference speed and 90.2% of the model size compared with the original model, which is more suitable for the application of practical scenes.
Research on Abstractive Summarization Technology Based on Deep Learning

Abstractive text summarization is an important research field in natural language learning. Compared with the extracted text summary, it is more in line with the preparation steps of the manual summary. In traditional machine learning, supervised learning is the main method. Text annotation, feature extraction, classification training, and weight extraction are also very different from those based on deep learning. Starting with the task of abstractive text summarization based on deep learning, this paper summarizes the current research status of abstractive text summarization, in-depth analysis of multiple designs of encoder-decoder for current deep learning methods, and points out the difficulties of current research and the hot direction of future development.
Feature matching and object registration based on convex hull alignment

Feature matching on target objects has always been a fundamental problem of the field of computer vision. It plays an important role in applications such as autonomous driving, 3D reconstruction, target recognition, and object registration can be formulated as matching image features to model features. Common feature matching algorithms like SIFT and FAST are mostly used in recent years. By searching for candidate feature points in the whole image, predefined descriptors are calculated for these feature points, and finally matching is performed between different images. Although over the decades, various improvements have made such methods more efficient and stable, and have proven successful in numbers of applications, they are still unstable in the face of occlusion and complex background. In complex scenarios, such methods always encounter problems such as time-consuming and multiple inaccurate matching. In this paper, we proposed an alternative approach to feature matching and object registration by aligning the convex hulls of target objects detected in images. At the same time, this method uses a descriptor based on the convex hull, which can guarantee rotation invariance and partial scale invariance. this alignment method uses a method derived from Convex Template Instance descriptor, which was originally proposed to fruit recognition and classification of segmented objects. We adopt this approach and improve it for the feature matching problem. The experimental result shows that this method can match the feature points on the 3D target quickly and accurately, also perform good robustness.
Human Body Movement Velocity Estimation Based on Binocular Video Streams

For estimating human body movement velocity in surveillance videos, a human body target detection and velocity measurement system based on binocular video streams is proposed. According to the imaging principle of binocular cameras, each frame of the binocular video stream consists of the left image and the right image. The left image is used as the input of YOLOv5 to detect the human body target and then track it by using DeepSort. Besides, the right image is an assistant to computing disparity and getting the distance of each pixel in the left image through some mathematical operations. Thereupon, the human body movement velocity is estimated in real-time. To analyze the accuracy of the velocity estimation, four different distance experimental scenarios are considered as the experiments. The estimation results have shown that the absolute error of the estimated velocity is less than 0.05m/s, which can satisfy the requirements of practical application.
Pointer Network Solution Pool : Combining Pointer Networks and Heuristics to Solve TSP Problems

This paper use pointer networks to improve the quality of initial solutions generation to solve slow convergence problems and the tendency to fall into local optimal solutions when solving path planning problems by the heuristic algorithm. The results show that the convergence speed and the optimisation outcome of the optimised algorithm are improved and can be effectively used to improve the application of heuristic algorithms such as VNS to the travelling salesman problem.
Circle Detection Through Nearest Distance

Object detection plays an important role in the field of image process. If the image is seriously effected by the light or contains different levels of noise, the detection will go wrong. The aim of this paper is to detect the circular spots in an image which is effected by an intensity of light. So in this paper, we apply a modified average filtering and binaryzation to get a binary image, and then get a edge binary image by applying an edge detection method named nearest distance method, and then we estimate the circle degree on the edge grids in the edge binary image, finally we obtain all the circular spots by filtering the circles according the circle degree threshold. We test such method on a concrete image and get a good performance on the circle detection.
Platform of the Web vulnerability detection Based on Natural Language Processing

Rule-based Web vulnerability detection is the most common method, usually based on the analysis of the website code and the feedback on detection of the target. In the process, large amount of contaminated data and network pressure will be generated, the false positive rate is high. This study implements a detection platform on the basis of the crawler and NLP. We use the crawler obtain the HTTP request on the target system firstly, classify the dataset according to whether there is parameter and whether the samples get to interact with a database. then we convert text word vector, carries on the dimensionality of serialized, through train dataset by NLP algorithm, finally obtain a model that can accurately predict Web vulnerabilities. Experimental results show that this method can detect Web vulnerabilities efficiently, greatly reduce invalid attack test parameters, and reduce network pressure.
RecommenderPlus: New Content-based User-centered Game Recommendation System

Many techniques have been incorporated into game recommendation systems, and they showed excellent accuracy in the recommendation. However, most of them are not user-centered, and users could only provide the game names to get their recommended list without any interaction. Therefore, we firstly conducted user research to evaluate what kind of factors will affect the games recommendation system. By analyzing the result of user research data, we concluded the influential factors our users genuinely care about. Then we designed a prototype/UI and deployed an application called RecommenderPlus, a user-centered steam games recommendation system based on the requirements of our users. Also, we introduced its design process and the challenges we met. After that, we analyzed and evaluated our model’s performance and discussed its advantages andlimits.
An Iterative Recovery Strategy based KNN Method to Edit the Data with Label Noise

Learning label noise, one of the most challenging tasks in supervised machine learning, has become a hot research topic recently. The k nearest neighbors (kNN) classifiers are sensitive to label noise and are often used to detect and filter label noise. However, relying on a single parameter k and single prediction result of kNN classifier, current detecting methods are prone to incur over-cleaning problems, resulting in the under-fit of the classification models. In this paper, we propose an iterative recovery strategy-based kNN method (IRS-kNN) to edit the training set with label noise. IRS-kNN has three significant advantages. Firstly, instead of relying on a single parameter k, it leverages a finite set of neighbors to detect the candidate label noise to improve the stability of the detected label noise; Secondly, by cascaded recovering the labels of the training data set for further detection, more difficult-to-learn label noise can be detected, enhancing the precision of the detected label noise. Thirdly, the iterative detection results provide meaningful guidance to deal with the label noise in different measures, which contributes to recovering the distribution of the corrupted data set as if it was not noisy. Experimental results on 8 UCI benchmarks demonstrate the effectiveness of the proposed method.
Research on Path-planning Combining the Salient Features of Landmarks and Photography Composition Techniques

In virtual roaming, traditional path planning algorithms only pursue short spatial distances and generate paths that are not really of interest to the visitor. In order for visitors to enjoy more pleasing scenery, the roaming paths need to be improved. In this paper, we first analyse the saliency characteristics of landmarks, and then combine the central composition and trilateration composition in photography to obtain a viewpoint quality field, while adding the Bezier interpolation method to obtain a local path with the landmark as the centre to guide the local camera movement. Finally, an improved Astar pathfinding algorithm is used to connect these local paths in an orderly and non-intersecting manner to obtain a global path with a beautiful landscape and a short path. The experiments show that the path obtained by this method is short in distance but high in scenic value and natural in transition, which is highly practical and applicable.
Research on salt body recognition based on DeepLab V3 combined with auxiliary classifier and attention module

Salt bodies are a class of salt-containing minerals, its density is lower than that of the surrounding rocks. Relative to other rock formations, it behaves differently in acoustic reflection. According to this property, images of salt-containing minerals can be obtained by using technical means. In the Salt Label tasks, the semantic segmentation model of deep learning can be used to achieve pixel-wise classification of images. There are fewer existing methods of salt body recognition, therefore, we proposed a method adding an auxiliary classifier and attention mechanism to the DeepLab V3 baseline, which is an effective salt recognition network. Experiments show that auxiliary classifier can speed up convergence and increase accuracy on large networks. The attention mechanism increases the accuracy rate while steadily accelerating the convergence rate. When the backbone network is the MobileNet, adding the above two structures can increase the PA of the original network by 2% and mIou by 4.1%. When the backbone network is ResNet50, the PA can be increased by 4.6%, and mIou can be increased by 11%.
Research and practice of blended teaching based on TVTHM supported by mobile learning platform

Blended teaching is a subversion and reconstruction of the traditional lecture-based teaching mode. It builds a new teaching mode with students as the main body and teacher guidance. With the help of a mobile teaching platform, a new teaching organization method that integrates physical classrooms and virtual classrooms. According to constructivism, situational cognition and learning theory, construct three vertical and three horizontal mixed teaching model (TVTHM), and design and carry out mixed teaching according to this model; fully apply the questionnaire Tool, introducing TAM model to analyze and compare learners’ abilities and blended teaching environment, design a six-dimensional teaching method, and carry out teaching practice in the course of “Self-learning Ability”. Knowledge, ability and quality have a significant effect. (Abstract)
Image Super Resolution Reconstruction Algorithm Based on Strong Constraint

To solve the problem of texture details loss in super-resolution reconstructed images, a novel image super-resolution algorithm (SCGAN) based on strong constraints with better fidelity was proposed. Firstly, in order to reconstruct finer texture details, an enhanced loss function was designed based on L1 function. Secondly, channel attention mechanism is introduced in super-resolution generative adversarial network architecture, and input feature weight parameters are given. Finally, the algorithm is validated on several benchmark data sets. The experimental results show that SCGAN not only has better visual effect, but also improves the objective evaluation index. Perceived similarity (LPIPS) improved at least 2.3% on average across the five test sets. Excluding RRDB algorithm with poor LPIPS, PSNR and SSIM improved at least 1. 2303dB on average and 0.0408 on average.
Studying the effects of image filters on extracting information reduction in meaningful image encryption

Meaningful image encryption based on DWT-SVD is an essential technique for steganography, and the method has been tested repeatedly and proven to be robust and secure. Under the premise of secure information transmission, it is pre-processing of the transmitted image before encryption enhances the robustness of the encryption process and enhances the confidentiality of third parties. In image pre-processing, whether the image distortion caused by the processing means affects the degree of restoration after extraction is studied. This study preprocesses the image with various filters to blur or remove unimportant information and highlight core information. Then the change of information entropy and PSNR value before and after the DWT-SVD blind watermark scheme is studied by comparison. It was found that the Gaussian Blur Filter processed image had a significant decrease in information entropy, and the details were blurred together with the background at the visual level. The Double Threshold filter performs better in terms of information entropy and PSNR values. The Canny Edge Detection Filter has stable information entropy and PSNR values, but it loses the features of the original image in the visual performance, making it difficult to identify. The results imply that the difference between the blur filter and the sharpening filter needs to be tested with more filters and images of different resolutions to reach a more accurate conclusion.
Spare parts consumption prediction based on AO-ELM

In order to overcome the limitation of small data samples in the prediction of spare parts consumption, which leads to low prediction accuracy, a prediction method of spare parts consumption quantity based on Extreme Learning Machine (ELM) was proposed. First, ELM algorithm was used to construct the prediction model of spare parts consumption quantity. Second, Aquila optimizer (AO) was introduced to optimize the ELM model performance and reduce the training time of the model. Finally, the superiority of the proposed method is verified by comparative experiments.
Sports Health Detection System Based on STM32

Nowadays, people pay more and more attention to health problems, and the health monitoring system comes into people's sight. People walk all the time, how to detect the health data when walking has become a hot technology. Based on this, this paper takes STM32F103 as the main controller and adopts hybrid multi-sensor fusion technology to construct the framework. Through pulse sensor, temperature core, ADXL345 tilt sensor and LM393 comparator module to obtain health data, a multifunctional health detection system is designed that can detect human heart rate, body temperature and walking speed in real time. Through experimental testing, the health detection system can accurately complete human data collection, data analysis and real-time data display through Kalman filter algorithm modification.
Lightweight convolution-based Chinese Speech Synthesis Method

Speech synthesis technology is one of the key technologies for human-computer speech interaction and is widely used in various fields such as audiobooks and information broadcasting. In this paper, we propose a speech synthesis model based on lightweight convolution (Lightweight Convolution-Tacotron2, LConv-T) to address the problems of severe long-distance information loss and slow inference in Tacotron2 speech synthesis model using recurrent neural networks. The encoder uses multiple lightweight convolutional modules connected in a densely connected manner to obtain the contextual information of the input text over long distances. To address the shortcomings of the proposed LConv-T model for speech synthesis, this paper further proposes a Tacotron2-based feature fusion speech synthesis model (Dynamic Lightweight Convolution-Tacotron2, DLConv-T), which can improve the stability of speech synthesis by using the Bi-LSTM and dynamic lightweight convolution modules respectively. The text feature extraction effectively improves the speech synthesis effect. The experimental results show that compared with the Tacotron2 model, the LConv-T and DLConv-T models reduce the objective evaluation MCD values by 0.15db and 0.42db, and improve the subjective evaluation MOS by 0.15 and 0.47, respectively.