Reviewerly/exHarmony huggingface dataset:
K-Means Clustering Algorithm Analysis on Specific Economic Development Problems in Target Countries

Most mainstream measures of economic development employ a weighted scoring system under the assumption that each indicator can perfectly substitute each other, which is a strong assumption that may vary from the real world. In this paper, the author uses the K-Means machine learning algorithm to cluster the 195 countries in the world, as an attempt to provide a more holistic view of each country&#x0027;s level of economic development without employing the assumption. With the assistance of silhouette scores, the algorithm created 6 clusters, each with its distinctive properties that future researchers or policy makers can rely upon to generate country-specific views about economic development. Nevertheless, manual inspection of the result discovers the potential problem with the incomplete datasets and the need for a PCA test to reduce dimensions. Considerations of realistic implications also suggest that the standard K-Means clustering might be over-simplifying the complicated nature of some country&#x0027;s economic problems.
Research on Machine Translation Strategy in Low Resources

The research goal of this paper is to solve the two main problems of current generative models used in machine translation: exposure bias and inconsistency of evaluation indicators in the training inference stage. Although supervised learning can solve these problems through some additional techniques, in low-resource translation tasks with relatively large search space requirements, model decoding has serious error transmission. Therefore, the reinforcement learning algorithm is applied to low-resource task processing. The algorithm can successfully solve the above-mentioned problems in the training process with a reward mechanism and be applied to the translation model.
Based on Evaluation of English Teaching Ability by Particle Swarm Optimization Algorithm under the Background of Smart Classroom Teaching Model

Smart classroom is the inevitable result of the deep integration of modern information technology and education. The use of particle swarm optimization algorithm can realize the process tracking of teaching and learning in and out of class, create an intelligent learning environment, and facilitate students to break through the limitations of time and space. Based the goal of improving English teaching ability, combined with the connotation and characteristics of smart classroom. Through evaluation of English teaching ability by Particle Swarm Optimization Algorithm this paper establishes smart classroom teaching model. Smart classroom teaching model for the coordinated development of teachers from the perspective of smart classroom teaching model exploration and application, in order to explore whether the teaching model can improve students' learning efficiency, reduce teachers' repetitive work and improve teaching effect. The conclusion of this paper can be employed to reference to promote the construction of English smart classroom teaching ability in vocational and technical institutes colleges, improve the quality of English teaching and promote the reform of English Teaching in vocational and technical institutes colleges.
Research and implementation of driving distance detection and warning system for automobile assisted driving based on Android platform

With the rapid development of mobile phones, it is possible to use computer vision recognition technology on modern smartphones to help detect driving and their distances. This paper studies and implements the detection of pedestrians and vehicles using Yolo on Android platform, and estimates pedestrian or vehicles distances based on its location data, and then implements a vehicle-assisted driving safety warning system.
Constructing Diversified online Teaching Based on Rain Classroom

In the first half of 2020, at the time of the epidemic, the national education entered the period of online teaching. The practical experience of online and offline mixed teaching done by the authors in the early stage provided a strong support for our online teaching. The most difficult part of online teaching is that teachers can't see students and students can't see teachers. Therefore, the effect of teachers' teaching has been affected and the atmosphere of students' learning is not strong. Based on the Rain classroom, the auther constructs diversified online teaching from four aspects: pre class preview, classroom teaching, homework correction and online feedback, stimulate students' interest in active learning, improve their ability to find, analyze and solve problems, so as to achieve teaching objectives. Through continuous conception and practice, good teaching results have been achieved. It will escort online teaching and provide good ideas for online and offline hybrid teaching.
Global Structured Feature Graph Convolutional Network for Skeleton-Based Action Recognition

With the development of human action recognition technology, deep learning has been applied to still images, and great progress has been made. However, in film action recognition, there is still the issue of using deep learning to improve the recognition rates. When predicting the action of a movie, encountering occlusions, large background changes, or accumulation of some errors in consecutive frames in the movie, resulting in a decrease in the accuracy of action recognition and increase the difficulty of film action recognition. In addition, there is a lack of structural information of bone joints and related research between two different structures. To solve this problem, this paper proposed a joint structure related feature network method using graph convolution network (GCN), which combines multiple convolution kernels of different dimensions to enhance the recognition rate of movie actions. The experimental database was established in the laboratory of Nanyang Technological University, Singapore. The system uses the NTU RGB+D motion recognition data set to evaluate our network. Preliminary experimental results show that our system may improve accuracy and make it more efficient.
Multi-layer Feature Fusion Method with Fewer Connections for Fast Semantic Segmentation

Feature fusion of spatial and semantic information is important to achieve high-performance semantic segmentation. However, fast semantic segmentation demands low computational complexity and challenges researchers to design structures efficiently. In recent years, Neural Network Architecture Search (NAS) has achieved better results in automatic network design. For lower computational complexity, we propose a multi-layer feature fusion with fewer connections in search space and add an improved penalty term for the loss function of the search algorithm to decrease the number of feature fusion connections. Based on the proposed multi-layer feature fusion method, we search the two-branch semantic segmentation model using the search algorithm reported by Gao&#x0027;s MTL-NAS. The experimental results tested on the Cityscapes dataset show that the searched module can improve the accuracy. For FastSCNN, ContextNet and BiSeNet, the mIoU improvement is 2&#x0025;, 2.5&#x0025; and 1&#x0025;, respectively. The searched module is also more efficient than the densely connected structure.
A Partitioning Method for Gamma Gaussian inverse Wishart Probability Hypothesis Density Filter using Kolmogorov-Smirnov Test

In multiple extended objects tracking, objects produce more than one measurement per time step. When objects are spatially close or maneuvering, the performance of typical partitioning methods will be reduced. This paper presents a partitioning method for gamma Gaussian inverse Wishart Probability Hypothesis Density (GGIW-PHD) filter based on the hypothesis test and the EM algorithm. The Kolmogorov-Smirnov test is modified to evaluate normality of the measurements. It provides guidance to the EM algorithm. The proposed partitioning method for GGIW-PHD filter performs well when the extended objects are spatially closed, and do not be affected by the inaccurate predicted components. Simulation results show that the proposed method performs better than existed algorithms.
An Image-based Measuring Technique for the Prediction of Human Body Size

To achieve high prediction accuracy of human body keeps an open issue for decades of years, especially when COVID comes and online retail becomes the major consumption channels. The body measurement is the key to solve cloth matching and recommendation in clothing e-commerce. This paper proposes a practical framework of image-based body measurement, by only taking the user&#x0027;s front and side photos. This framework does not require pure background or precise standing position, and supports manual modification of the measurement results. The framework takes people&#x0027;s height, weight and gender as params to initialize a common body size set, and corrects each part of the set by analyzing the body proportion via the front and side images. The prediction accuracy was tested with the 50 digital models and 10 real people. Results showed that the circumference sizes such as chest, waist, hips, have errors less then 5&#x0025;, while the length sizes such as arm, leg approach to actual length on net body models. For real people, the errors depend on the wearing clothes. In addition to high accuracy, the method has a rapid process speed, reaching 19QPS on a NVIDIA RTX5000 GPU server.
A Graph-Convolutional-Network based Prototype Mixing Model for Few-shot Segmentation

Over the past few years, deep convolutional neural networks (CNNs) based semantic segmentation methods reached the state-of-the-art performance. To train a model with the ability to know a concept, a lot of pixel level annotated images are required, which is time consuming and hard to cover unseen object categories. Thus, few-shot semantic segmentation has been developed to implement segmentation with a few annotation images. In this paper, we proposed a novel prototype mixing model for few shot segmentation. Different with other works which only produce prototypes form support set, our proposed model learn a group of concept-specific prototypes from support set and then generate prototypes from query set. With prototypes from both query set and support set, we proposed a GCN(Graphic Convolutional Network) module to generate mixing prototypes for better utilizing of informations from different categories. We also proposed a clustering module to produce multi-prototypes for representing different parts of a single semantic class, which reach better performance than single prototype. Our model achieve 48.8% and 55.9%mIoU score on PASCAL-5i for 1-shot and 5-shot settings respectively.
DF-VLAD: Deepfake Video Detection based on Feature Aggregation

With the rapid development of Deepfake technology, face video forgery can produce highly deceptive video content and bring serious security threats. The detection of this kind of fake video is more urgent and challenging. Most of the existing detection methods regard this problem as a common binary classification problem, and using a simple average or maximum as the prediction of video results can easily lead to missed detection or false detection. While the video-based detection work such as LSTM, in Deepfake detection, too much focus on timing modeling will affect the performance of Deepfake video detection to a certain extent. Based on this, this paper proposes a VLAD-based aggregation module DF-VLAD, which advances the aggregation of multiple frames from the output layer to the feature layer, which on the one hand makes the aggregation more flexible, on the other hand, it also uses the objective function of forgery detection to directly guide the learning of frame-level depth representation; On the other hand, this paper deals with this problem as a special fine-grained classification problem, because the difference between fake face and real face is very subtle. It is found that the existing face forgery methods such as Face2Face and NeuralTextures leave some common artifacts in the spatial domain. Different forgery methods produce different artifacts, while natural faces have more similar features. To make the model pay more attention to artifacts, a forgery trace capture model based on the fusion of self-attention mechanism and channel attention mechanism is proposed in this paper. Like other fine-grained classification methods, note intentions are used to guide the network to pay attention to key parts of the face. Experimental results on different public data sets show that the proposed method achieves the latest performance.
Application and existing problems of computer network technology in the field of artificial intelligence

With the development of science and technology, computer technology is more and more widely used in people&#x0027;s life. As a branch of computer technology, artificial intelligence technology is also gradually developing and expanding, affecting people&#x0027;s life. Nowadays, the processing ability shown by simple computer technology has gradually lagged behind the times. If you want more convenient data processing, artificial intelligence is an excellent solution. Agent technology in artificial intelligence makes artificial intelligence have stronger data processing ability and learning ability than computer technology, and can make decisions quickly according to data information. Artificial intelligence technology can promote automation and intelligence in various industries and improve the production efficiency of enterprises. Starting from the elaboration of artificial intelligence, this paper deeply explores the relationship between artificial intelligence and computer, analyzes the development status of artificial intelligence and computer, and puts forward the technical problems between artificial intelligence and computer, so as to provide reference for the further development of artificial intelligence.
Span-adaptive Transformer for the Cascade Relation Triple Extraction

Recently, the high effectiveness of cascade binary framework has been proved in overlapping relation extraction. However, few researchers are applying this framework to Chinese samples and the position encoding in its encoder is not well adapted to the Chinese input sequence. This paper which aims to promote the study of overlapping relationships for Chinese proposes a new positional encoding method for Transformer and constructs a new model called SaTRE (Span-adaptive Transformer for the Cascade Relation triple Extraction). First, we take the character or word segmentation results of the input sequence as a span and concatenate all the spans into a new sequence. Second, we find that the absolute relation of span(position)-to-position(span) and the relativity between positions are equally essential for entity span extraction in the Chinese sequence. Hence in the self-attention module of the encoder, SaTRE decouples attention, representing span(position)-to-position(span) correlation with absolute head-tail positional encoding and positional correlation with span-adaptive relative positional encoding. Third, we investigate whether the original periodic coefficient of Sinusoidal Function positional encoding certainly has better effectiveness. In conclusion, the effectiveness of the proposed method has been proved through extensive experiments and ablation studies.
Generating Adversarial Examples for Robust Deception against Image Transfer and Reloading

Adversarial examples play an irreplaceable role in evaluating DNNs&#x0027; security and robustness. It&#x0027;s essential to understanding the adversarial examples&#x0027; effectiveness to utilize them for model improvement. In this paper, we explore the impact of input transformation on adversarial examples. First, we discover a new phenomenon. The process of RELOAD or TRANSFER may deactivate adversarial examples&#x0027; malicious functionality. The reason is that these processes would reduce pixel precision, which counters the perturbation added by the adversary. We validate this finding on different mainstream adversarial algorithms. Second, we propose a novel Confidence Iteration method, which can generate more robust adversarial examples. The key idea is to set the confidence threshold and add the pixel loss caused by image reloading or transferring into the calculation. We integrate our solution with existing adversarial algorithms. Experiments indicate that such integration can significantly increase the adversarial attacks&#x0027; success rate.
A Texture Enhanced Feature Fusion Method for Small Object Detection

Due to the increasing demand for search and rescue of people at sea, our requirements for the performance of the object detection of high-definition pictures taken by drones have also become higher. Since the target is very small, it is very challenging. Most of detectors are based on feature pyramid network (FPN) to enrich the common features of shallow features by combining deep context. However, because small targets are at the low level of FPN, the semantic features are very small, and the texture features are not obvious. Therefore, this paper proposes a texture-enhanced feature fusion network (TEFF) to solve this problem. Our model is mainly composed of three parts: channel attention module (CAM), texture enhancement (TE) and feature fusion (FF). Among them, CAM mainly completes two parts. The first is to generate feature heat maps to enhance features, and the other is to generate adaptive parameters for hierarchical fusion. TE and FF are mainly for feature enhancement and adaptive fusion between levels. Specifically, the whole functional level is enhanced and feature fusion is carried out through the self-attention mechanism. Our network has achieved good results on multiple backbones.
Research on Mixed Teaching Mode under the Background of the reform of teachers, teaching materials and teaching methods

During the epidemic period, in response to the national education policy of “continuous suspension of classes”, the teaching work of universities was carried out online with the help of various online teaching platforms such as Superplatform, rain classroom, smart tree, university MODC, school-based platform smart classroom, as well as well as software with the function of teaching live broadcast. In order to improve the teaching effect, build students ‘cognitive system, cultivate students’ ability of independent learning and collaborative learning, and help students to achieve the knowledge objectives, skills objectives and quality objectives required by the curriculum. The author from the current situation of the post-epidemic era, combined with hunan automobile engineering vocational college online mixed teaching reality, with research results at home and abroad as the theoretical support, develop granular micro class video, system design orderly teaching link, in the actual practice of offline teaching mode, and reversed transmission teachers, teaching materials, teaching method reform.
SRM-based Service Discovery Method in Information System Architecture Design

In Military Information System Architecture design area, after the design of the operational view, service related data need to be designed to support the completion of operational activities. Existing research lacks methods to support the design of service related data an. In this paper, the service discovery method based on semantic relation matrix(SRM) is proposed. First, according to the logical relationship between operational activity and operational nodes, the relation is transformed into the SRM. Then, according to the overall semantic relationship, internal semantic dependency, external semantic dependency and average semantic relationship, the clustering results are obtained according to the mappings between the corresponding operational activities and operational nodes in the SRM. Finally, the service cluster generation method based on simulated annealing algorithm is used to determine the result of clustering as a service. Case illustrates this method based on SRM.
Multi-class Sentiment Analysis

The popularity of the web and mobile devices, people can generate messages anytime from there to express their opinion and emotions. The job of sentiment analysis is to analyze the emotional state of the person who left the message. Sentiment analysis can be two-class or multi-class, in this paper, we propose a method of extracting sentiment words for multi-&#x00AC;class sentiment analysis and use the Ministry of Education Dictionary as supplement to expand the sentiment words according to the extracted sentiment words in the training dataset. The experimental results show that the accuracy of our approach is close to the best method and higher than the other methods for multi-class sentiment analysis.
Unsupervised cross-domain target detection and recognition system based on deep

The application of target detection and recognition technology in the industry has promoted the further development of computer vision technology. However, for target detection in complex environments, the influence of shooting angle, character pose and lighting environment in different scenes leads to unsatisfactory target detection performance, inconsistent distribution among different scene data domains, and difficulty in migrating existing models to be applied in new target domains. Therefore, for the complex environment, how to effectively solve the problem of model reuse under unsupervised conditions, this paper proposes an unsupervised cross-domain target detection and recognition system based on deep migration learning, which provides a simple operation interface, and through simple and convenient operations can quickly deploy the target detection environment in complex environments, realizing the detection, localization, early warning alarm and logging of vehicles, people, etc. in complex environments storage, etc. Finally, the reliability of the system in practical applications is verified through target detection and character recognition experiments.
A Projective Noise Reduction Algorithm Based on Laplacian Smoothing

In order to avoid the curse of dimensionality, which is often encountered in big data analysis, manifold learning assumes that high-dimensional data is embedded in a low-dimensional smooth manifold. However, in practical applications, the data sets often contain inevitable noise, and the manifold learning algorithm itself is often sensitive to noise. Therefore, in order to reduce the interference of noise to the manifold learning algorithm, we propose a new projective noise reduction algorithm based on Laplacian smoothing. The main idea of this algorithm is to perform Laplacian smoothing on a data set before projection of the sample points, so that the data set can present expected manifold structure, which solves the problem that the sample points are too scattered and deviated from the main manifold well. In avoid of the over-smoothing problem caused by Laplacian smoothing algorithm, we propose to control the number of iterations based on the variance reduction. The experimental results show that the proposed algorithm can reduce the noise as well as maintain the manifold structure, and the noise reduction effect is superior to the traditional noise reduction algorithm.