Reviewerly/exHarmony huggingface dataset:
A Personalized News Recommendation Algorithm Integrating User Comments

With the explosive growth of the number of news, how to help users efficiently and accurately find the news they are interested in from the mass of news has become more and more important. The traditional collaborative filtering news recommendation algorithm is based on user browsing records. However, the user's browsing history cannot directly indicate the user's preference for news. Meanwhile, the degree of correlation between user comments and news can intuitively reflect the degree of interest of users in news. Thus, a personalized news recommendation algorithm incorporating user comments is proposed. Firstly, the algorithm proposes a calculation method of user similarity, which integrates the influence of news popularity and the text characteristics of user comments. Secondly, we integrate the correlation between comments and news into the calculation of user's news preference, and measures the confidence of user's news browsing records according to the correlation level. Finally, we evaluate our approach with a large number of experiments. The experimental results show that the personalized news recommendation algorithm combined with user comments has significantly improved the recommendation effect compared with other algorithms.
Analysis of E-commerce Review Text Based on Capsule Network

For traditional models such as CNN, BIGRU, lacks the representation of sentence system features and lacks the ability to extract deep features and other problems. This paper took the e-commerce Chinese reviews as the research objects, in the BERT word vector and attention mechanism, an sentiment analysis is proposed based on the capsule network model. First, the model uses BERT to obtain a vector, then uses CNN to extract deep-level phrase features, then uses BIGRU to extract global features deep-learning phrase system features to obtain the features of the sentence system; and then uses the increase of the attention layer to filter the effective features. Finally, the model uses a richer expression of text sentiment information to improve the overall abstraction ability of the model. After comparing experiments on the dataset, this method is better than the control group, indicating that the proposed model can effectively improve the accuracy of text classification.
End - to - End Image Steganography Scheme Based On $\mathrm{U}-\text{Net}++$ Structure

After introducing neural network into steganography, it based on neural network has been effectively improved compared with traditional steganography. As people pay more attention to neural networks and research more deeply, more and more excellent network models appear. These network models were introduced to further improve steganography. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{U}- \mathbf{net}++$</tex> is the integration of U-Net with different depths, and the jump connection in the decoder part has also been redesigned, and the feature map is densely connected. While training <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{U}- \mathbf{Net}++$</tex> , multiple U-Nets at different depths within the structure can stimulate collaborative learning among the components. Image quality is higher with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{U}-\mathbf{Net}++$</tex> than U-Net. We introduce <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{U}-\mathbf{net}\ ++$</tex> into the hidden network and propose a new steganographic network as the main part of the hidden network, using this network to embed the color cover image with the same size into the gray secret image. The constructed network has two functions of hiding and extracting, which are realized by two parts respectively. The sender completes the information hiding process by hiding network, The sender completes the information hiding process by hiding the network, and the receiver completes the information extraction process by extracting the network. The experimental results of this paper show that our proposed network can hide secret information and still have high invisibility.
A Self-attention Hybrid Network for Aspect-level Sentiment Classification

Aspect-level sentiment classification is a task to determine the sentiment polarity of an aspect target in a sentence. In this paper, we propose a hybrid network based on self-attention mechanism, including self-attention convolutional neural network module and target toward sentence attention module to achieve sentiment classification. Specifically, the self-attention convolutional neural module can explore the remote context semantic information, overcoming the shortcomings of the traditional convolution neural network that can only obtain local features because of the limitation of the convolution kernel and pooling. In the target toward sentence attention module, the attention network could compute the hidden state of the sentence obtained by bidirectional gated recurrent unit (Bi-GRU) and the target embedding matrix, and further obtain the important part of the sentence for the target. In addition, in order to express the relative relationship, we add location embedding to the input layer. Experiments on restaurant and laptop datasets show self-attention hybrid network (SAHN) achieves state-of-the-art accuracy relative to other models in aspect-level sentiment classification.
Research on cloud computing resource scheduling based on improved ant colony optimization algorithm

With the rise of cloud computing services around the world, how to reduce the load balancing degree of cloud computing resource scheduling and improve the utilization rate of cloud computing resources has gradually attracted the attention of the academic community. In order to meet these needs, I proposed to use ant colony optimization algorithm scheduling to deal with large-scale cloud computing resource data sets. Meanwhile, in order to prevent the ant colony optimization algorithm I proposed from falling into the local optimal solution, I proposed to improve the probability and heuristic factor of selecting the next node of ant colony optimization algorithm as well as the update of pheromone, so as to give full play to the guiding role of pheromone to achieve the optimal cloud computing resource scheduling scheme. Examples have proved that the algorithm proposed by me has low relative standard deviation and load balancing degree. Meanwhile, the proposed scheduling scheme can evenly allocate resources, and the total utility value of scheduling is high, which can improve the scheduling ability of cloud computing resources to the greatest extent. At the same time, the fewer iterations required to schedule resources, the better the convergence of the algorithm. It can be seen that this algorithm can significantly improve the potential of cloud computing resource scheduling.
Moving Object Tracking Method in Dynamic Image Based on Machine Vision

Moving object tracking in moving images is an important research topic in the field of machine vision. It is widely used in military and civilian fields, and has important research significance and value. In this paper, a moving target tracking method based on machine vision is proposed. On the basis of understanding the difficulties of moving target tracking, the information acquisition of moving target dynamic image is completed through input and digitization steps; The region segmentation method is used to extract the features of the collected target information; Detect the target information based on yolov3 algorithm; Finally, the kernel correlation filter is used to track the target. The experimental results show that the P and R values of this algorithm are slightly lower than those of KCF and SA algorithms, but the FPS value is higher than those of these two algorithms, It shows that this method has certain application value.
Breast Cancer Classification Based on Various CNNs and Classifiers

Breast cancer is the second leading cause of death from cancer in women around the world. The CAD system utilizing machine learning and deep learning techniques facilitates the early detection of breast cancers. However, few recent studies focused on utilizing multiple feature extractors to compare and analyze the performances of various architectures. This paper analyzes the performances of architectures which are combinations of different feature extractors and classifiers in breast cancer diagnosis. Firstly, we collected histopathological breast cancer images from the BreakHis dataset. Secondly, the normalized data were converted to one-hot encoding for training, validating, and testing. Thirdly, we used VGG-16, VGG-19, Xception, ResNet50, Inception-V3, and Inception-Resnet-V2 to extract features. Next, fully connected layer (FCL), logistic regression (LR), and SVM were employed to classify breast cancers on the BreaKHis dataset. The experimental result shows that with the cyclical learning rate (CLR) policy, the ResNet50-SVM model obtained the optimal accuracy rate of 93.9% on eight-classification. The result shows that our proposed method could diagnose breast cancer with high accuracy.
A Study on the Evaluation of English Classroom Learning Status Based on Expression Recognition Technology

With the advent of the era of big data and the continuous and in-depth development of artificial intelligence technology, various networks as well as network models have emerged, ushering in the era of artificial intelligence in education. Emotion recognition technology, as a key technology of educational AI, is helping to transform education. This study combines the characteristics of English classes and proposes an expression recognition technology-based student learning status evaluation method for English classroom teaching, which can provide teachers with intelligent and comprehensive teaching feedback and enable them to grasp students' learning situation in class in a timely manner, and has strong practical value for English classroom teaching.
Analysis of Java Lock Performance Metrics Classification

Java locking is an essential functionality and tool in the development of applications and systems, and this is mainly because several modules may run in a synchronized way inside an application and these modules need a good coordination manner in order for them to run properly and in order to make the whole application or system stable and normal. As such, this paper focuses on comparing various Java locking mechanisms in order to achieve a better understanding of how these locks work and how to conduct a proper locking mechanism. The comparison of locks is made according to CPU usage, memory consumption, and ease of implementation indicators, with the aim of providing guidance to developers in choosing locks for different scenarios. For example, if the Pessimistic Locks are used in any program execution environment, i.e., whenever a thread obtains resources, it needs to obtain the lock first, which can ensure a certain level of data security. However, it will bring great CPU overhead and reduce efficiency. Also, different locks have different memory consumption, and developers are sometimes faced with the need to choose locks rationally with limited memory, or they will cause a series of memory problems. In particular, the comparison of Java locks is able to lead to a systematic classification of these locks and can help improve the understanding of the taxonomy logic of the Java locks.
A Hierarchical Information Sharing Platform for Students Based on the Internet of Things and Artificial Intelligence Systems

Existing college student information systems have various problems, and cannot meet the operational needs of college guidance departments, enterprises, and students. With the gradual development and adjustment of the employment market for college students, the operational needs of college guidance departments, enterprises, and students are also constantly developing and deepening, which puts forward further requirements for system functions, performance, and scalability. The main purpose of this paper is to conduct research on the hierarchical information sharing platform for students based on the Internet of Things and artificial intelligence systems. This paper mainly analyzes the current situation of student information management and the database design principles of the information sharing platform, and constructs, implements and tests the platform functions of the cloud platform system. The test results show that the information sharing platform meets the school's accurate positioning and management requirements and can be used normally.
Research on Parallel Data Mining Based on Spark

In the current era of big data, the rapid development of network technology and hardware equipment leads to exponential data growth. However, under the challenge of massive data, there are still some problems in the field of data mining, such as low efficiency of algorithm execution, insufficient parallel optimization of algorithms and poor usability of data mining platforms. This paper focuses on parallel data mining algorithms and parallel data mining tools. Based on Spark as a programming model and processing engine, a distributed parallel data mining scheduling framework is designed and implemented based on Hadoop and Spark, which can meet the needs of users for mining and analyzing large data sets. The scheduling system implements common data mining algorithms such as classification, prediction, clustering and data preprocessing, and can complete data mining modeling by visual drag and drop algorithm program.
Restoring Information With Pyramid Recepting Block in Monocular Depth Estimation

Monocular depth estimation is an active filed in computer vision. Most of the depth network follow the form of encoder and decoder. A lot of detail is lost when the image passes through the encoder, but the decoder can not restore the information. Original U-net using skip connections to help decoder to restore information, but the effect is not very well. In this paper, a Pyramid receptor block (PRB) was proposed and the skip connections are redesigned, which effectively improved the accuracy of deep network.
Deep Fake Image Detection Based on Decoupled Dynamic Convolution

At present, deep forge image detection algorithm has some problems such as unknowable content, insufficient generalization ability and heavy computation when detecting samples of multi-scene and multi-forgery techniques. To solve these problems, a depth forged image detection method based on decoupling dynamic convolution is proposed. Firstly, the random brightness contrast was set, and the abnormal information of the enhanced image was used as the input of the model. Then, the decoupling dynamic convolution is introduced into the residual network, and the abnormal features of the forged image are captured by channel dynamic convolution. The location of abnormal features is extracted by spatial dynamic convolution, and the effective abnormal features are extracted by synthesizing channel information and spatial information of the image to reduce the calculation cost. Finally, the full connection layer is used to detect the depth forged image. The experimental results show that the new method can distinguish the true and false images efficiently when the data sample scenes are diverse and the forging methods are abundant. The average accuracy of the new method is better than that of the comparison method, and the calculation cost is lower than that of the comparison method.
Comparative Study of Various Lightweight Mask Recognition Algorithms

As an important line of defense against novel coronavirus, masks can effectively reduce the risk of novel coronavirus infection. In this paper, three algorithms were used for mask wear detection, respectively using the opencv native library, MTCNN+MobileNet, and pyramidbox_lite_mobile_mask in paddlehub. Finally, the test results of the three algorithms were analyzed and compared, and the experimental results are that the pyramidbox_lite_mobile_mask model in paddlehub has the most sensitive face recognition and mask detection ability, which can identify the blurred face and judge whether to wear a mask, followed by MTCNN + MobileNet.
Self-attentive mechanism-based supervised comparative learning

To address the intra-class diversity and inter-class similarity issues in traditional contrast learning, this paper proposes a supervised contrast learning based on a self-attentive mechanism that can effectively increase the feature extraction ability. The proposed method consists of two stages: feature encoder pre-training and linear classifier fine-tuning. In the feature encoder pre-training phase, the supervised contrast loss exploits the labeling information of the data to minimize the distance between similar images in the embedding space and maximize features of different categories as far away as possible, enhancing the effect of contrast learning. Beyond that, the self-attentive mechanism-based block is introduced in the encoder module to explicitly build the interdependence between the convolutional feature channels and further improve the feature learning capability of the model. In the linear classifier fine-tuning stage, parameters of pre-trained encoder are fixed and only the classifier is fine tuned for the downstream classification task. Experiments on the CIFAR-10 and CIFAR-100 datasets demonstrate the superior of our proposed method.
Research on Temperature Field Control Method of Heat-in-Place Regeneration Based on Finite Element Radiation Heat Transfer Model

The control technology for the heating temperature on asphalt pavement is one of the basic elements to ensure the good effect of asphalt pavement hot-in-place regeneration. However, the construction site lacks certain technical solutions for the temperature range control of the asphalt pavement hot-in-place regeneration. As a result, the temperature field of asphalt pavement hot-in-place regeneration is uneven, the temperature of the pavement fluctuates, and the temperature range of asphalt pavement hot-in-place regeneration of the same road surface is not uniformly heated. As a result, recycled asphalt pavement has large quality defects. In order to avoid the above problems, this paper studies the temperature field of hot-in-place regeneration of asphalt pavement in detail. Detailed mathematical modeling is carried out from the heat transfer model angle and the pavement's heat radiation propagation. Then, the temperature field control method for asphalt pavement hot-in-place regeneration is designed in detail to avoid the above problems.
Innovation Research of RBF Algorithm on University Information Management in Big Data Era

The third industrial revolution has brought us into the “information age”, and the new technology revolution marked by “information” is developing rapidly. During this period, the information management of college has made great contributions to the development of the curriculum, and brought about major changes in teaching methods, teaching methods, curriculum strategies, and curriculum settings. This essay aims to study the innovative research of RBF algorithm on university informatization management in the era of big data. This essay firstly introduces the innovation of college management in the era of big data, mainly explores the development of informatization learning and network teaching. This essay uses the RBF neural network algorithm to build a new university information management system to improve the efficiency and security of university information management. Experiments have proved that the simulation error of the system constructed in this essay is within 5%, which can effectively improve the resource allocation of the system. In the case of multiple people logging in and using in parallel, the response speed of the system is about 0.3s, the response is fast and the relatively stable.
High Dimensional Missing Data Imputation for Classification Problems: A Hybrid Model based on K-Nearest Neighbor and Genetic Algorithm

Repairing the missing data is important for the data analysis because the existence of missing data may lead to the deviation of results. Most of the previous studies focused on the repair accuracy and neglected the calculation amount. Currently, the increasingly big data results in the heavy workload and limits the application of previous methods. Therefore, a two-stages hybrid imputation model (GA-GKNN) was proposed based on the K-nearest neighbor (KNN) and genetic algorithm. First, the genetic algorithm was used for the feature selection. Then, an updated KNN with gray correlation degree (GKNN) was proposed to increase the accuracy. Hence, the proposed model can achieve an ideal accuracy with a significant low calculation cost because it only needs some key feature information to impute missing values. The model effectiveness is validated by an experimental analysis using the real-world datasets in UCI database. Compared with the traditional KNN and GKNN imputation models, the average imputation error is reduced by 0.1425 and 0.0181, and the average imputation time is reduced by 89.72% and 83.34%. It maintains a favorable balance between the calculation cost and accuracy for the imputation problem with a wide missing rate, which possesses the potential in the applications.
Application of Artificial Intelligence in the Development of Media Integration under the Background of Smart Media

Smart Media uses artificial intelligence technology to reconstruct the entire process of news information production and dissemination, creating a good environment for the development of high-quality media platforms. This paper studies the application of artificial intelligence in the development of media convergence in the context of smart media from three aspects. Among them, media content monitoring based on speech recognition technology can improve monitoring efficiency, meet the needs of media content monitoring, and create a more intelligent monitoring technology system. The interactive design based on augmented reality technology generates virtual information with the help of computers, creates an environment where virtual and reality blend, and provides people with a more complete information experience. The stereoscopic display design based on virtual reality technology makes the virtual environment closer to the real environment by introducing various simulators, so that realistic stereoscopic images can be seen.
Vehicle Recognition Based on Improved Faster R-CNN

The objects detected by the existing object detection algorithms are bounding boxes with a certain background, and the bounding boxes cannot be aligned with the objects. In order to solve the pixel alignment problem and improve the average accuracy, this paper introduces a mask based on the Faster R-CNN algorithm, which can extract the fine spatial layout of the target, so as to distinguish the target from the background and improve the average accuracy. For vehicle recognition, the improved model is trained on the vehicle attribute classification dataset, and the average accuracy of classification recognition is significantly improved. The vehicle recognition algorithm proposed in this paper is based on the improvement of Faster R-CNN, and the effectiveness of the method is verified by testing with the COCO dataset.