Reviewerly/exHarmony huggingface dataset:
Get more Emotional Words with Word Vectors

Sentiment words play an important role in sentiment classification research. In sentiment analysis and classification research, sometimes it is necessary to obtain as many sentiment words as possible. Open source word vector datasets usually have a huge vocabulary that contains rich sentiment words. Sentiment words can be obtained using vectors and a large number of words in open source word vectors. In this paper, given sentiment words, such as sentiment words in the dictionary, are used as seed words, and then the word similarity algorithm is used. From the open source word vector data set, the words similar to the target sentiment words are searched, and the sentiment words are obtained through manual labeling and filtering. Experiments show that this method is an efficient and low-cost method to obtain sentiment words.
Self-Supervised Low-Light Image Enhancement based on Retinex Model

Self-supervised learning does not require any paired low-/normal-images in training, which greatly improves its application range. However, the existing self-supervised learning methods only set a single target brightness value, which reduces the brightness of the highlights in the image, resulting in image artifacts. In this paper, we propose a self-supervised learning method based on Retinex theory for low-light image enhancement. We convert the image to HSV color space to reduce the loss of color information, and the proposed value estimation network is used to generate the final reflectance(R), which is treated as the enhanced brightness component, combined with the hue(H) and saturation(S) component, and then converted back to the RGB color space to obtain the final enhanced image. In addition, we introduce an attention module to reduce artifacts caused by setting a single target brightness value. Experiments demonstrate that our method outperforms multiple existing state-of-the-art methods.
A Certificateless Authenticated Key Exchange Protocol Based on Ring Learning with Errors

Ring learning with errors (RLWE) is used to construct anti-quantum key exchange protocols cryptography algorithms, which is hard to solve for quantum computers. In this work, we provide a certificateless authenticated key exchange protocol based on RLWE problem. This proposed protocol implements user identity authentication by certificateless public key cryptosystem, and uses Ding error reconciled function to eliminate the error caused by the introduction of interference term in RLWE problem. We analyzed the correctness of the protocol in detail, and proved the security of the protocol under the improved extended Canetti-Krawczyk (eCK) security model.
A Closely Spaced Objects Super-Resolution Method Using Array Camera Images

In the detection of spaced objects, the detected objects are usually far from the imaging system, resulting the weak radiation intensity and limited imaging area. Specially, the aliased projection of closely spaced objects further reduces the resolution of imaging results and restricts the subsequent high-level tasks. To solve this problem, a newly imaging system with array cameras has attracted more and more attention, which can provide complementary information of objects from multiple perspectives. In this paper, we study the geometric relationship between closely spaced objects and array cameras, and propose a closely spaced objects super-resolution method based on sparse reconstruction of array camera images. Thanks to the prior assumption of the spaced objects' sparsity and the transfer constraints between views of array cameras, our method can well super-resolve the obtained image and effectively estimate the number and location of the closely spaced objects.
Book Image Distortion Correction Based on Bezier Curve Model

With the development of information technology, image acquisition is becoming an important means to obtain information. Taking pictures of books, using OCR technology can quickly obtain the information of books, but in practical application, the book page will be distorted, which will affect the accuracy of information collection. In view of this situation, on the basis of comparing the correction methods of Bezier curve and machine learning, this paper focuses on the correction effect of text image using Bezier curve under different distortion degrees, and uses the rigid features of the page to transfer the correction model to the text illustrations on the same page, and finally sends the corrected text into OCR recognition, Comparing the recognition accuracy before and after correction, through the result analysis, using Bezier curve bending correction has better correction effect and efficiency without increasing hardware resources.
Comparison of Satellite Target Recognition Methods

The structure of space targets is usually simple and symmetrical. For some simple space targets, such as spheres and cylinders, the analytical expression of the variation of radar cross section (RCS) with attitude angle can be obtained. Taking the RCS time series of satellite targets as a random process and extracting its statistical characteristics can realize the recognition of space targets. In view of this idea, the satellite target recognition comparison is proposed, the system is designed to realize the automatic processing of the satellite target RCS data simulated by FEKO software, and the recognition comparison of different satellite data is carried out to verify the recognition effect of satellite targets under different classifiers.
Research on Application Configuration Management Technology for Cloud Platform

With the increasing popularity of cloud computing platforms, more and more application services are being deployed to the cloud platform. However, due to the increasing number of cloud applications and changing requirements, the configuration information of cloud applications is gradually scattered and difficult to manage. How to effectively achieve rapid deployment and management of cloud application configuration information has become one of the main challenges that restrict the stable and reliable operation of cloud applications. Therefore, this paper studies the configuration information management technology of distributed cloud platform, and proposes a set of cloud platform configuration information management technology solutions that can sense the host configuration status in real time, standardize the expression and management of configuration information categories, and visually analyze the configuration information situation, which can effectively solve the above problems and improve the high reliability of cloud applications.
Cloud Resource Scheduling Algorithm Based on Combination Weight

The default scheduling algorithm in the cloud environment only considers the two performance indicators of CPU and memory, and uses a unified weight to calculate the candidate node score, which cannot meet the needs of different Pod applications. Disk and IO rate are three indicators, and the subjective weight and EW (entropy weight) are calculated by AHP (analytic hierarchy process, analytic hierarchy process), and resources are calculated in real time according to the resource utilization of node performance indicators during Pod application deployment process The objective weight of the indicator. The combination of the two weights is applied to the improved TOPSIS (technique for order preference by similarity to an ideal solution) multiple attribute decision-making method to select suitable candidate nodes. With the increase in the number of deployed Pods, in the case of a large cluster load, the standard deviation of the comprehensive load is significantly improved compared with the default scheduling algorithm of Kubernetes.
Development of artificial intelligence robot industry in the era of big data

With the advent of the era of big data, the research and development level of artificial intelligence has gradually improved, and the application of robots in all walks of life has gradually become popular. Artificial intelligence robot industry has the characteristics of high technology intensity, talent intensity and capital intensity, and also occupies an important guiding position in the international scientific and technological development. This paper studies the development of China's artificial intelligence robot industry in the era of big data, explores the problems existing in the development of artificial intelligence robot industry, and puts forward suggestions, so as to better promote the development of China's robot industry, improve productivity, and accelerate the development of traditional and emerging industries.
An Analysis of Asteroid Mining and Global Equity Based on Two-dimensional Vector Evaluation Model

It is generally believed that asteroid mining will be feasible in the future. When that day comes, we will need a suitable way to allocate those resources because it involves global equity. In this paper, we mainly analyzed the issue of global equity and the future of asteroid mining, along with how they are related to each other. In this paper, we gave our definition of global equity. In order to evaluate global equity, we then collected the data of 9 indicators for 32 countries over a 10-year period and divided these indicators into two dimensions: internal power (IP) and external power (EP). We combined entropy weight method (EWM) and group decision method (GDM) to calculate the weight of these indicators in a two-dimensional space. Then, a two-dimensional vector evaluation model was established to classify global equity into four categories: equitable, relatively equitable, inequitable and extremely inequitable. Vector (1,1) was used as the standard to evaluate global equity. The vector formed by the two-dimensional value was placed in the two-dimensional coordinate system and the angle 0 of the vector can represent the state of global equity. The K-means clustering model was used to divide 32 countries into three categories. Meanwhile, six representative countries were selected to conduct a comprehensive evaluation of global equity based on the regional development of six continents.
Remote Sensing Image Object Detection Based on Oriented Bounding Box and Yolov5

Target detection on remote sensing images is an important task of data interpretation. Because the targets in remote sensing image are usually arranged in any direction, and the size varies greatly, this makes general target detection algorithms have good detection result in natural scenes, but it is not ideal in remote sensing images. Aiming at the problems of arbitrary arrangement and multi-scale targets, a rotation-based network with oriented bounding box was proposed based on the single-stage detection network YOLOv5. Firstly, an angle prediction regressor is added to the output layer of YOLOv5, which contains an aspect ratio perception weight function to solve the sudden change of angle loss caused by square-like targets. Secondly, BiFPN (weighted bidirectional feature pyramid network) structure is used to achieve simple and fast multi-scale feature fusion. Experimental results show that the proposed algorithm achieves an accuracy of 76.62% on DOTA dataset, which has a higher accuracy compared with other advanced algorithms. And the algorithm significantly improved the detection accuracy of square targets.
Research on garbage classification and recognition method based on improved GMM model

Classification and recognition is the precondition of garbage classification. The traditional manual classification and recognition is inefficient, and it is difficult to meet the requirements of garbage classification in modern life. At present, there are still many problems in the research of intelligent garbage classification and recognition, such as fuzzy classification of solid waste, low recognition of moving objects, slow running of program and so on A hybrid model (GMM) for garbage classification and recognition is proposed, Four types of solid waste graphic database including hazardous waste, recyclable materials, kitchen waste and other waste were constructed, and the data set was annotated. Aiming at the situation of huge amount of garbage data, complex shape and even partial overlap, this paper proposes to study the recognition problem of garbage classification on the slow-moving pipeline. By comparing GMM, SVM and k-NN models, GMM model is selected and improved. GMM model will consume large amounts of system resources when building background model. At the same time, it is easy to produce the phenomenon of ghosting when the long-time stationary target turns to motion. In order to solve the above problems, the number of Gaussian distribution function established by the pixel is simplified, which can be adjusted automatically. The moving target detection algorithm is improved and the ghosting is eliminated. Using Halcon software to build deep learning system to classify and identify the target, using multi thread reading to improve the efficiency of image processing and program execution.
An Electromagnetic Environment Assessment System and Method for Civil Aviation Airport

In order to solve the shortcomings of the electromagnetic environment assessment technology of the existing civil aviation airports, this paper proposes a civil aviation airport electromagnetic environment test and assessment system and method. That is to obtain the spectrum data of complex electromagnetic environment signals around the planned radio station through the spectrum data acquisition module. Then, the Background noise and interference signals can be calculated and assessed after spectrum data fusion by computing unit in the electromagnetic environment assessment system, and finally the electromagnetic environment assessment report of the planning aeronautical radio station site is given. Provides the basis for safe operation and assignment of frequencies.
Parallel Semantic Fusion Image Caption Generation Analysis Theory

Image caption is a multimodal task, whose purpose is to input an image and automatically generate coherent sentences from the visual information of the analyzed image to describe the content of the image. However, in most models fusing image information with semantic information, the extracted visual information is insufficient. In this paper, a parallel double-layer LSTM(d-LSTM) is proposed as a decoder to process semantic information. The semantic information obtained from the hidden state of the first layer is used as the primary information of the semantic information generated by the second layer. Finally, the semantic information of the two layers of decoders is fused to generate a finer-grained image caption. The superiority of our proposed model is verified by large-scale experiments on MSCOCO datasets.
Generating Interested Captions in Image with Visual Semantic Concepts

A novel image captioning method fusing visual semantic concepts is proposed in this paper. Before generating captions, a concept generated by a classification is provided in order to inform our model the interested topic of image contents. Different from end-to-end deep networks, we generate captions through two stages. Based on the predicted visual concept, a local relevant region is predicted. We construct a sequence of region features which are generated by enlarging the detected local regions. The final caption is obtained through a Bi-directional LSTM based model. The performance of our model is evaluated on Visual Genome data set. The experimental results demonstrate that our model could effectively provide image information that users really focus on.
Risk Assessment Method of Microgrid System Based on Random Matrix Theory

In view of the problems that the existing power grid risk assessment mainly depends on the data fusion of decision-making level, which has strong subjectivity and less effective information, this paper proposes a risk assessment method of microgrid system based on random matrix theory. Firstly, the time series data of multiple sensors are constructed into a high-dimensional matrix according to the different parameter types and nodes; Then, based on random matrix theory and sliding time window processing, the average spectral radius sequence is calculated to characterize the state of microgrid system. Finally, an example is given to verify the effectiveness of the method.
Game Image Quality Enhancement Algorithm based on Generative Adversarial Network and Knowledge Distillation

Nowadays, with the development of computer graphics and the improvement of related hardware, the picture quality of games is getting higher and higher. Today's mainstream games support resolutions up to 4k or above. At the same time, for a large number of excellent games in the past, the picture quality is far behind today due to technical and hardware limitations at the time. Blurred picture quality is becoming more and more unbearable for users. For this reason, the game company will spend a lot of resources to reset the game to meet the needs of these users. The combination of super-resolution technology based on deep learning and generative adversarial networks can effectively improve image resolution. In this paper, we combines ESRGAN(Enhanced Super-Resolution Generative Adversarial Networks)to design a real-time game image quality enhancement algorithm, and distilled the model, reduce parameters for increase the game frames.
Double Loss Block Neural Architecture Search

Neural architecture search aims to automatically design network architectures by machines, which is expected to bring about a new revolution in machine learning. Despite high expectations, the effectiveness and efficiency of existing neural architecture search solutions are unclear, and the inefficiency of neural architecture search solutions is mainly attributed to inaccurate architecture evaluation. In this work, this paper divides the large search space of neural architecture search into multiple modules, effectively reducing the error caused by shared parameters to ensure that the candidate architectures in the modules are fully trained. By adopting a block search method, this paper can Evaluate all candidate architectures in a module, addressing the problem of inaccurate evaluation. In addition, high-quality data is necessary. Under the guidance of the teacher model, combined with the ground truth, the neural architecture search is supervised by the double loss function. Under the constraint of the same amount of computation, the method in this paper achieves 77.3% top-level accuracy on ImageNet., which is 1.0% higher than the efficient network EfficientNet-B0, and 0.3% higher than the DNA method without additional computation. Therefore, the double loss block neural architecture search model proposed in this paper significantly improves the effectiveness of neural architecture search.
Fault prediction model of cloud platform based on long short-term memory network

In the process of implementing recommendation, the time sequence of users' browsing and following page information is important data information in the recommendation algorithm, and the same user's different preferences for items at different times also have a certain impact on the recommendation results. Under the framework of the filtering model, it is proposed to integrate the long-short-term memory network and generalized matrix factorization to capture the user's short-term and long-term preferences at the same time. Using the long-short-term memory network's strong fitting ability to time series data, learn the user's short-term preference information, The long-term dependencies of sequences are captured, and the long-term preference information of users is learned through generalized matrix factorization, so as to optimize the recommendation algorithm and improve the recommendation performance. It is verified by experiments that the new model proposed in this paper has improved both in terms of convergence speed and recommendation performance.
Single Space Object Image Denoising and Super Resolution Reconstructing based on Unpaired images

High quality space target image is of great significance for space attack and defense because space exploration missions are becoming more and more important. But high quality images of space object are difficult to obtain due to the large number of cosmic rays in the space environment, as well as the limitations of optical lenses, detectors and transmission links on satellites. Image denoising and super-resolution reconstruction are the most economical and effective methods to solve this problem. This paper presents an unpaired denoising and super-resolution reconstruction method for optical remote sensing images which could obtain the images has higher quality than dataset itself. In order to further improve the quality of optical remote sensing images, high quality natural images are added into the training set, and unpaired image data sets (natural images and optical remote sensing images belong to different fields and cannot correspond one to one) are adopted to complete the training of the whole network by using the idea of unsupervised learning. Through the verification tests of three optical remote sensing image data sets, it can be seen that the method in this paper has reconstructed high quality optical remote sensing images with higher resolution than the dataset itself.
Generative Zero-shot Image Classification based on Generative Feature Constraint

Zero-shot learning (ZSL) utilizes semantic information that is auxiliary information to transfer knowledge from seen classes to unseen classes, thereby realizing the recognition of unseen classes. The generative methods train the model to generate pseudo image features for the unseen classes, thus transforming ZSL into a supervised learning problem. However, the classification performances of these methods still suffer from weak generalization. To address this issue, the paper proposes a novel Generative Adversarial Network based on Generative Feature Constraint (GFCGAN). First, based on the LsrGAN [1], a semantic constraint module is added to restrict the generated image features by keeping the semantic information consistent. Then, in the case of keeping the semantic relationships between classes consistent, the generated image features of unseen classes are farther away from the feature prototypes of their most similar classes in both semantic and feature space so as to train the generative model and generate high-quality pseudo image features. Finally, the trained classifier is used to classify images of unseen classes. Experiments on standard datasets verify the effectiveness of our model and achieve good results.
Non-word Strings in Open Source Word Vectors

The Chinese open source word vector corpus is an important corpus for current research and application. Due to the imperfect word segmentation method, it contains a large number of non-word strings, which brings difficulties to the application. Investigating and analyzing such strings in open source word vectors can provide valuable reference information on how to handle them. This paper uses a widely used open source word vector corpus containing more than 8 million: the Tencent word vector corpus, and conducts a preliminary analysis on the non-word situation by combining manual annotation and rule-based query technology. Experiments have found that the open this source word vector corpus contains a large number of non-word strings, especially the strings in the word vector entries with three-character words and above, about 80% are not words. Obviously, this brings a lot to the use of Huge damage. We analyze the specific situation of this kind of harm with the example of word similarity application.