Reviewerly/exHarmony huggingface dataset:
IAFormer: A Transformer Network for Image Aesthetic Evaluation and Cropping

Aesthetic quality evaluation of images has an important role in the field of visual analysis, and the widespread use of high-quality image editing has gradually increased the importance of image aesthetic evaluation in automatic image processing tasks. Previous researchers have mostly explored the mapping relationship between images and labeled scores using convolutional neural networks, but the aesthetic features of different regions on images have not been explored sufficiently, when an image is rich in background information and it is necessary to correlate the aesthetic features of different regions to evaluate the image, convolutional neural networks often cannot extract the aesthetic features of the image adequately due to the lack of the advantage of global feature modeling. We introduce a novel Transformer architecture for image aesthetic quality assessment(IAFormer), IAFormer can model the global aesthetic features of an image, and it is a framework that unifies the aesthetic quality assessment of images and the aesthetic cropping of images, while the aesthetic quality of the image is evaluated, the aesthetic weights on different patches within the image can be calculated to give valid reference information for the aesthetic cropping task.
An Improved K-Means Algorithm Based on Impact Index

The k-means clustering algorithm is a very classical clustering algorithm that is widely used because of its excellent efficiency and performance. The algorithm uses Euclidean distance to calculate the similarity between samples and iteratively updates the membership matrix to obtain clustering results. However, when k-means algorithm clusters datasets containing samples with intra-cluster distances greater than inter-cluster distances, errors often occur when partitioning the boundary samples, which eventually leads to unsatisfactory results. Moreover, although k-means algorithm makes the intra-cluster distance as small as possible, it neglects to maximize the inter-cluster distance, and eventually only finds the local optimal solution. Different from the existing k-means type algorithm, this paper proposes a similarity measure based on the impact factor, which determines the partitioning result by comparing the impact of samples on each cluster. And on the basis of the objective function of k-means algorithm, we combine the inter-cluster distance to solve the defects of local optimality that exist in k-means algorithm. In the paper, we theoretically analyze and prove the proposed method, and compare and analyze the clustering results of the algorithm with the class k-means algorithm on real datasets, and confirm that the proposed algorithm in this paper can effectively avoid the defects of the class k-means algorithm.
Person-Frame Dynamic Feature Graph Network for Group Activity Recognition

Dynamic modeling of different dimensional features in video is the key element of group activity recognition. In the past years, a lot of work has been devoted to the modeling of character features, these methods have achieved good results, but most of them ignore that group activity is a continuous motion closely related to the scene, and underestimated the importance of the relationship between frames. This paper proposes a Person-Frame Dynamic Feature Graph Network to model group activity information from two levels: video frame level and individual level: Temporal Semantic sub-Graph (TSG) channel constructs temporal semantic relation subgraph for video frame features, and Person-level Dynamic Feature Map (PDFM) models personal dynamic characteristics. In addition, in order to alleviate the problem of slow training speed of group activity model, we use lightweight mobilenet-v2 as the backbone, and embed the Initial Feature Preprocessing Module (IFPM) in it to improve the training efficiency while maintaining the recognition accuracy. A lot of experiments have been done on this model with the most widely used dataset in the field of group activity recognition, and excellent results are obtained, which proves the effectiveness of the model.
Research on the Optimal Selection Method of Fuzzy Semantics in English Long Sentence Machine Translation

In order to improve the accuracy of English long sentence machine translation, the fuzzy semantic optimal selection model of English long sentence machine translation is constructed by combining fuzzy semantic optimal selection and feature extraction methods. The fuzzy semantic optimal selection model of English long sentence machine translation based on adaptive learning of machine neural network is proposed, and the constraint object model of fuzzy semantic selection of English long sentence machine translation is constructed. The method of context correlation mapping is used to analyze the fuzzy semantic features and construct the ontology structure model in the process of English long sentence machine translation. The linear mapping and statistical information analysis of English long sentence machine translation are realized by using the linear semantic ontology structure mapping mechanism and the corresponding text sequence parameter mapping in the dictionary, and the language semantic correlation calculation model of the optimal selection of fuzzy semantics in English long sentence machine translation is established. The machine neural network adaptive learning method is adopted to realize the segmented learning control of the non-sentence backbone in the process of fuzzy semantic selection of English long sentence machine translation. Weighted learning and adaptive weight analysis are realized according to the machine neural network adaptive learning result of the optimal selection of fuzzy semantic of English long sentence machine translation, and the optimal design of fuzzy semantic optimal selection model of English long sentence machine translation is realized. The simulation results show that this method is robust and the evaluation result is accurate, which improves the accuracy and anti-interference of English long sentence machine translation.
Adaptive Class-Balanced Loss Based on Re-Weighting

As real-world data grows fast, the problem of data imbalance has become more prominent. Thus the long-tail problem in deep learning has received lots of attention recently. One of the solutions is to apply a class rebalancing strategy, such as directly using the inverse of the class sample size for reweighting. In past studies, the setting of weights only relates to the number of class samples. Only relying on the information of the number of class samples to determine the size of the weight is very crude in the sensitive method of re-weighting. In this paper, we implement adaptive re-weighting for three essential attributes of the dataset considering several factors: the number of classes, the number of samples, and the degree of class imbalance. We conducted experiments on the commonly used sample imbalance problem solution and proposed a new sample reweighting method. Specifically, a novel re-weighting idea is proposed to optimize Class-Balanced Loss Based on an Effective Number of Samples. Experiments show that the method is superior in re-weighting imbalanced datasets on deep neural networks. We hope our work will stimulate a rethinking of the number-of-samples-based convention in re-weighting.
Different-Order Fixed-Time Zeroing Neural Networks for Time-Varying Linear Matrix Equation

Zeroing neural networks (ZNN) have gained popularity in solving linear matrix equations in recent years, which are produced by defining an indefinite error function. As a result, different ZNN models can be created using various error functions. In order to determine the time-varying linear matrix equation, two first-order ZNN models and a second-order ZNN model are proposed in this research. We investigate a weight sign-bi-power function to activate the proposed three ZNN models in order to achieve fixed-time convergence. Additionally, for the three proposed ZNN models, we theoretically determine the upper bounds of the fixed-time convergence. Finally, the effectiveness of the three proposed ZNN models is examined and contrasted using two simulated cases. The simulation outcomes demonstrate that the second-order ZNN model performs better than the first-order ZNN model.
Semantic Similarity Evaluation Method Based on Text Generation Data Augmentation

The similarity evaluation method based on neural network has achieved good results, but it has higher requirements on the scale and quality of the corpus. Based on this problem, this paper proposes a semantic similarity evaluation method based on text generation data augmentation. This method combines Seq2Seq with a masked language model for data augmentation, and uses the expanded data to fine-tune the pre-trained language model. The pre-trained language model and the Siamese network are combined to build a semantic similarity evaluation model. Finally, experiments were carried out on the standard sentence similarity evaluation data set SentEva12012-2016. Compared with the benchmark model, the Spearman correlation coefficient improved by 3.11%. Experiments show that the semantic similarity evaluation method based on data augmentation can effectively solve the problem of low accuracy due to lack of data.
Research on UGV Path Planning in Tunnel Based on the Dijkstra*-PSO* Algorithm

Nowadays, the researches on UGV path planning mainly are mainly related to the interwoven road environment of the city or the relatively flat and broad environment in the field. As a kind of obstacle models that can not be ignored in the process of movement, the research on UGV path planning in the tunnel has considerable necessity. In this paper, aiming at the internal path planning problem of obstacles such as tunnels, a motion path planning scheme of unmanned ground vehicles (UGVs) based on Dijkstra- particle swarm optimization (Dijkstra-PSO) is proposed. Firstly, the appropriate obstacle curve function is selected according to the characteristics of the obstacle model; Then the Dijkstra algorithm is improved to realize the initial screening of the number of path nodes through a node screening rule, and the initial optimized path can be obtained by using the improved algorithm; By using the dynamic step size to adjust the value spacing and taking the maximum available step size as the influence factor to evaluate whether the optimization of tentative point is stagnant or not, the PSO algorithm is further improved to avoid generating redundant optimized nodes. Finally, an improved PSO algorithm is proposed to re-optimize the initial optimized path. The results show that both the Dijkstra algorithm and the hybrid algorithm can realize path planning without collision and obtain the optimal path from the starting point to the end point. Compared with only using one algorithm, the hybrid algorithm (the Dijkstra*-PSO* algorithm) can effectively shorten the path length and improve the quality of the path searching. In the experiment, compared with the experimental results of the single improved algorithm (the improved Dijkstra algorithm and the traditional PSO algorithm) and the double improved algorithm (the improved Dijkstra algorithm and the improved PSO algorithm), the path length obtained by the latter one is shorter and the number of path nodes is smaller, which indicates that the double improved algorithm can meet the requirements of the later application of UGV to carry out collision-free movement in similar tunnel obstacles.
Speech Augmentation Using Conditional Generative Adversarial Nets in Mongolian Speech Recognition

Aiming at the problem of uneven regional distribution of speech caused by the lack of labeled data in the Mongolian speech data set, this paper proposes a Mongolian speech data augmentation model based on a conditional generation confrontation network. The model uses conditional speech generators and multiple fusion discriminators for adversarial learning, and uses Mongolian text and specified regional features to generate Mongolian speech with specified regional features. The original data set was augmented by using the methods of speech rate perturbation and spectrogram enhancement, and compared with the end-to-end Mongolian speech recognition model trained on different augment data sets and the original data sets, it was found that the word error rate in the end-to-end Mongolian speech recognition model trained on the augment data set of the specified regional characteristics is 3.1%; Compared with the end-to-end Mongolian speech recognition model trained on the original data set, the speech rate disturbance data set, and the spectrogram enhancement data set, the word error rate dropped by 2%, 0.5%, and 0.8%.
A Vehicle Re-Identification Method Based on Fine-Grained Features and Metric Learning

To solve the problem that the global features extracted by the ResNet-50 network has insufficient recognition capability in similar vehicle re-identification task, a new Re-ID method combining metric learning is proposed. Firstly, the fine-grained features of vehicles are extracted by using triplet constraints, and then combined with the global features extracted by the backbone network as vehicle features. Secondly, the similarity of different vehicle features is judged and ranked by Euclidean distance, so as to obtain more accurate results. Finally, a comparative experiment is conducted on the VeRi-776 dataset for different network models. The results show that our method has high recognition accuracy in Re-ID tasks. Compared with ResNet-50, the mean average accuracy (mAP) is improved by 2.30 %, rank-l increased by 2.31 %, and the rank-5 increased by 2.05 %. It is verified that this model can effectively improve the recognition accuracy in vehicle Re-ID.
Research on Secure Data Sharing Technology of Block Chain

Blockchain technology has attracted much attention since its emergence. Its unique characteristics of decentralization, trustworthiness and tamper-proof provide the possibility to build a more secure and effective data sharing platform. This paper first discusses the relevant knowledge of data sharing technology, explains how block chain realizes data sharing, and then analyzes existing data sharing schemes. It is also classified according to its core technology, so that researchers can quickly understand the existing data sharing schemes based on block chain, and can judge and choose research direction and technical route according to their own needs. This is also the value of this study. Finally, this paper analyzes the performance of four shared data schemes using experimental data from literature, and predicts the future development of sharing technology.
Research on Lexicalization and Ordering Methods of Hierarchical Phrases in Chinese-English Machine Translation Software

Aiming at the problem of poor regularity of hierarchical phrases distribution in Chinese-English machine translation software, this paper constructs a lexical ordering model of hierarchical phrases in Chinese-English machine translation software to improve the accuracy of Chinese-English machine translation software translation. This paper proposes a lexical ordering method of hierarchical phrases in Chinese-English machine translation software based on dynamic reusability and structured partition fusion. This paper constructs the rule type distribution set of Chinese-English machine translation software hierarchical phrases, adopts the dynamic compilation method of rules to realize semantic feature detection and sparse parameter identification of Chinese-English machine translation software hierarchical phrases, and adopts the multi-dimensional semantic network grouping feature sorting and dynamic detection method to cluster the target language monolingual corpus of Chinese-English machine translation software hierarchical phrases. This paper establishes the entity structure model of bilingual corpus that takes into account diversity, realizes semantic feature enhancement and information fusion after the combination of the translated text and the original bilingual corpus through tensor expression of data clustering, realizes grouping and filtering of interference data through multi-dimensional scale extended clustering processing of hierarchical phrases of Chinese-English machine translation software, and rearranges structured data of hierarchical phrases of Chinese-English machine translation software by link-based clustering method, thus realizing lexicalization and reordering of hierarchical phrases of Chinese-English machine translation software. The simulation results show that this method has good anti-interference performance and high accuracy of lexicalization, which improves the ability of extracting and identifying the lexical information of hierarchical phrases in Chinese-English machine translation software, thus improving the accuracy of Chinese-English machine translation software.
ASHN for Multi-Human Pose Estimation

Due to the diversity of human body posture, there are problems such as occlusion of key points, difference of target scale and background blur among people. Therefore, multi-human pose estimation is still a challenging task. The existing deep learning-based multi-body pose estimation methods are mainly divided into top-down and bottom-up, but most of them do not make full use of local features in the network. In this paper, convolutional block attention module(CBAM) and Focal L2 Loss were used to process the context information of convolutional neural network and consolidate local features. Specifically, we propose attention-containing stacked hourglass network (ASHN). ASHN is based on a stacked hourglass network, with the addition of a convolutional block attention module (CBAM) module to improve performance, combined with Focal L2 Loss in the model. Compared with the existing methods, our method achieves competitive performance, achieving 66.8% AP, 72.1% AP75 and 65.4% APM on COCO data sets.
The Use of Explainable Artificial Intelligence in Music—Take Professor Nick Bryan-Kinns’ “XAI+Music” Research as a Perspective

This paper mainly uses the comparative analysis method and the case analysis method to explain the explainable artificial intelligence (XAI) in music. The use of XAI in music is real-time interactive, and the more explainable and transparent, the more accurate it is. Interpretability can occur in or after modeling, and deep learning provides theoretical support for XAI. Professor Nick Bryan-Kinns' team made a breakthrough through experimental research on “XAI+Music” with the difficulties currently being explained by artificial intelligence, XAI technology has been directly applied to the creative AI model, for “XAI+Music” development and innovation provide references and ideas.
Fine-Grained Sentiment Analysis Based on Heterogeneous Graph Neural Network

Fine-grained sentiment analysis is a key task of sentiment analysis that aims to identify emotional polarity in a particular aspect of a sentence. Existing research has shown that dependency information can improve the performance of sentiment analysis task. In recent years, many studies have begun to focus on global dependence information, and these studies have also significantly improved the performance of emotion analysis tasks. In this paper, we present the GLo-GCN method. Specifically, it uses the results of dependent analysis to learn local dependence information weight, on the whole corpus to build words and document heterogeneous graph, and the heterogeneous graph neural network further divided into more fine-grained graph neural network learning global dependent information weight, eventually fusion local dependence information weight and global dependence information weight for emotional analysis. Performance on two public datasets demonstrates the effectiveness of our model over state-of-theart models.
A Rule Embbeding Method of Winrate Approximation for Texas Hold’em

Expected winrate is one of the most important features in Texas Hold’em, and it is the foundation of state estimation and decision-making. Calculation of the expected winrate needs to consider the unknown opponent’s hole cards and the undealt board cards. The huge combination space of these unknown cards makes the enumeration method time-consuming. A solution is using neural network to approximate the true winrate, but the calculation of the input features from cards information is fairly complex. In order to avoid feature engineering and improve the computing speed, a new neural network method based on rule embedding is proposed. With the elaborately designed convolution/max-pool kernel and a new proposed hypo-max-pool operation, the cards category rules are efficiently embedded into the winrate fitting network with several nodes. The result of experiments indicates that our rule embedding neural network method greatly outperforms the feature engineering method on the prediction accuracy and the computation efficiency.
Research on Reward Distribution Mechanism Based on Temporal Attention in Episodic Multi-Agent Reinforcement Learning

In episodic multi-agent reinforcement learning, we study how to distribute episodic rewards at the end of an episode by trajectory time steps to obtain high-quality and interpretable learning reward signals to guide agent training. This paper proposes a reward distribution method based on temporal attention. This method is used to characterize the influence of actions on state transitions along the trajectory, and the effect of each influence on the global reward at each time step. This method is combined with a deep deterministic strategy, and a multi-agent algorithm based on the temporal attention reward distribution mechanism is proposed to speed up the convergence speed of the agent. The effectiveness of the algorithm is verified in a multi-agent pursuit environment. Compared with other reward distribution methods, the method in this paper converges faster and obtains higher rewards.
Collaborative Filtering Recommendation Algorithm Based on K-Means and GCN

In the internet age, various contents flood people’s internet life, causing information redundancy, so performing more useful information extraction becomes an important task. Among the recommendation algorithms, the most common one is the collaborative filtering algorithm, which has the problem of data sparsity when performing matrix construction due to the poor relationship between users and items, which affects the effectiveness of recommendations. To address the data sparsity problem, the thesis proposes a collaborative filtering recommendation algorithm (KGCF) based on K-Means and GCN, which introduces K-Means and GCN, using the ability of K-Means to aggregate data and the ability of GCN to extract features in non-Euclidean space to obtain the hidden relationships between users and items, and populate the similarity matrix of users and items to alleviate the The paper uses the MovieLens dataset to improve the recommendation performance of traditional collaborative filtering algorithms. The paper uses the MovieLens dataset for comparison experiments, and uses MAE as the evaluation metric. The results show that this paper’s algorithm is better than similar algorithms in solving the sparsity of collaborative filtering data.
Improved Weighting K-Means Algorithm Based on Covariance Matrix

Feature weighted clustering algorithms are an important direction of research in cluster analysis. However, the existing weighted k-means algorithms have some problems that the influence of noise features cannot be excluded and there exist some defects in the use of feature weights. To solve these problems, this paper firstly uses covariance matrix to weight the features of data sets; then this paper presents a theoretical analysis and a proof; lastly, the clustering results of the proposed algorithm were compared and analyzed with existing algorithms on a real datasets, which shows that the proposed algorithm can effectively eliminate the influence of noise features and improve the clustering performance. Through this research, the proposed algorithm can effectively improve the clustering performance and has a wide range of application prospects.