Reviewerly/exHarmony huggingface dataset:
Automatic Generation of Sentences Expressing Stock Price Trends Using Stock Prices and Earnings Reports

In this research, we propose a method to automatically generate sentences expressing stock price trends using stock prices and earnings reports as part of the automatic generation of Nikkei 225 market summary articles. The sentences expressing stock price trends generated by our method are as follows: “Retail, wholesale, and construction-related industries are rising. Machinery, steel, and electrical equipment-related sectors fall.” Our method estimates investment themes, such as stock market trends, businesses, products, and social backgrounds, using stock prices and earnings reports. Specifically, our method extracts groups of companies that have experienced large fluctuations in stock prices, and then refines the groups of companies by clustering based on keywords extracted from earnings reports of each company. Next, our method estimates investment themes from the keywords in the narrowed-down group of companies, and generates sentences expressing stock price trends based on the investment themes.
Joint entity and relation extraction with part-of-speech-aware attention and dependency parsing embedding

Joint entity and relation extraction is an important task in natural language processing, whose purpose is to obtain all triples in text. However, the existing models seldom pay attention to the part-of-speech (pos) of each word and the dependency parsing (dp) in the sentence. To solve these problems. a joint extraction model with part-of-speech-aware attention and dependency parsing embedding is proposed, named PADPE. The proposed model obtains better word representation through pos-aware attention mechanism. In addition, the parts of speech and dependency characteristics are integrated respectively in entity classification and relation classification to improve the accuracy of the classifier. The experimental results demonstrate that our model can solve the overlapping triple problem more effectively and outperform other baselines on three public datasets.
A camouflage generation algorithm based on modified K-means clustering

The advent of high-tech means of detection posed a huge challenge to traditional camouflage imaging. It is important to develop a more efficient and better performing digital camouflage algorithm to improve the poor camouflage effects. The performance of camouflage generation is mainly affected by the camouflage color and camouflage texture. In this paper, we propose a novel design of digital camouflage based on he K-means clustering optimized by genetic algorithm. First, we randomly call the plaque of the target neighborhood to retain texture details, and then smooth the removal of abrupt boundaries. Then, we extract primary colors from the background and precisely reduce the influence of randomization of the initial cluster center using a clustering method. By comparing with the other reported camouflage patterns, we find that the output camouflage patterns generated by our proposed method greatly match the background and have good camouflage effect.
A method for aggregating the features of bullet screen

Bullet screen is a new type of comment appearing on video websites. When the video is played, it will be displayed in real time. Through sentimental analysis of the screen, we can quickly understand the sentimental types of the current video. However, it is unrealistic to deal with all the bullet screens of the whole video at one time with traditional methods. This paper proposes a method of aggregating all text content in video to represent very long video content to achieve video feature extraction. The method proposed in this paper is more effective than PCA and scalable methods in reducing dimension and achieving aggregation.
DCL-CGAN: a co-modulation generative adversarial network combined with the dual contrastive loss function for image de-occlusion

With the advancement of deep learning, image restoration technology has also made great progress. Most of the commonly used networks adopt encoder-decoder architecture, in which the long-term correlation between pixels can only be expressed by a deep convolutional layer. However, this method cannot guarantee the stability of the network, because the multi-level cooperative work of the network is the basic framework of the GAN network; the extremum-extreme formula and the gradient of alternating up and down make this unstable phenomenon more serious. In order to solve this problem, a co-modulation mechanism that supports remote modeling across image regions is added to the GAN model. At the same time, the traditional logistic loss function leads to insufficient generalization of the feature representation of the discriminator, which cannot stimulate the adversarial evolution of the generator, and it is easy to forget previous data pattern and the previous task . In response to this problem, this project will use the dual contrastive loss mechanism to avoid problems such as damage to the semantic structure of the generated samples or failure of the generated distribution mode. By combining these methods, the performance of PSNR, SSIM, and FID indicators for image restoration has been improved.
A vehicle re-identification method based on random occlusion

In the computer vision field, vehicle re-identification research is an important direction. There have been many researchers doing research on vehicle re-identification. But they mostly focus on the overall image and ignore occluded images, which may be more practical and challenging. This paper proposed a re-identification method to solve the occlusion in remote sensing image, which extracted the pixels in the random rectangular area of the image and covers them to other positions of the image. The feasibility of the proposed random occlusion method was proved by experiments on simulated occlusion images. And experiments on real occlusion image data sets showed that the proposed random occlusion method was better than the random erasure method in solving the occlusion problem, and can obtain better re-identification results.
Data Hiding using Slantlet Fusion Method and T-Codes

This paper presents the study of steganography algorithm based on Slantlet fusion method using a self-synchronizing variable length encoder, viz., T-codes. The Slantlet fusion method is the high bit rate data hiding scheme based on Slantlet Transorm. In this method, the fusion process takes place between the SLT of the secret data and the SLT of the cover image. The secret data is obtained by the application of SSVLC, T-codes to the original data. The proposed algorithm is compared with the Wavelet fusion method using the metrics PSNR, PSNRE, PSNRY, CQM, SSIM and KLdiv respectively. It is found that the proposed scheme is highly secure and efficient in comparison to the state of the art works.
Evolution of GPU virtualization to resource pooling

With the rapid development of artificial intelligence, the demand for Graphics Processing Unit (GPU) resources is also increasing rapidly. To improve the efficiency and utilization of GPU resources, virtualization technology has been widely used in the field of GPUs. This article reviews the evolution from GPU virtualization to resource pooling, including device simulation, GPU pass-through, hardware-assisted virtualization, GPU full virtualization, GPU remote-sharing, and GPU resource pools. The advantages and disadvantages of each stage and the challenges faced during the evolution process are also analyzed. This article discusses the difficulties and solutions of GPU resource pool construction, as well as the construction steps and framework, providing a reference for the research and application of GPU resource pooling.
Blockchain-based MQTT communication access control scheme for the Internet of Things

The Message Queuing Telemetry Transport (MQTT) protocol is commonly used in the Internet of Things (IoT) due to its simple and efficient communication method. However, this also brings some security risks, such as arbitrary device publishing and subscription that can cause the IoT system to malfunction. Traditional MQTT access control methods, such as using access control lists (ACL), have some problems, such as security issues in storing permissions and matching efficiency. In this paper, we propose a blockchain-based access control method for IoT MQTT communication, which uses decentralized blockchain to store access control information, solves the problem of single-point failure, and ensures information security. To address the MQTT permission matching issue, we propose a Patricia Trie based adaptive priority topic search tree (PTST). This data structure optimizes the storage consumption of permission information and, for MQTT wildcard matching, uses an adaptive priority permission matching approach, which always searches from high to low priority, greatly improving the matching efficiency. Then we propose an access control model and process based on eXtensible Access Control Markup Language (XACML) standard. In the end, we perform performance testing and comparison with other data structures, and the results show good performance of PTST, demonstrating the feasibility of our scheme.
Automated gadget discovery in the quantum domain

Abstract In recent years, reinforcement learning (RL) has become increasingly successful in its application to the quantum domain and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent’s learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent’s policy.
Incorporating chunking information for Chinese named entity recognition using neural networks

Most named entity recognition (NER) systems use supervised machine learning methods, including linear models and neural networks. However, both methods require large amounts of annotated data. In this paper, we utilize chunking resources to improve the performance of NER without annotating more data and investigate two approaches to incorporate chunking information in the character-level neural network framework for Chinese NER. The first approach is multi-task learning, which falls into the framework of sharing common feature representation of Chinese chunking and NER task. The second approach is based on stacking, which uses the features provided by a trained chunking model to guide the NER model. Experimental results on OntoNotes 4.0 corpus show that compared with the models only using the NER corpus, the NER models which utilize the chunking resources can improve the performance of NER.
Research on semi-trailer panoramic surround view system based on feature point matching

Based on the structural characteristics of semi-trailer trains and the problem of large blind spots in their field of view, this article proposes a pure visual solution to determine the articulation angle technology and build a panoramic view system for semi-trailer trains to solve the problem of blind spots. Firstly, the fisheye camera is corrected for distortion and perspective transformation, and a weighted fusion algorithm is used to generate partial panoramic images of a single vehicle body. Then, the articulation angle algorithm based on feature point matching is used to obtain the articulation angle information, and the panoramic view image of the entire vehicle is generated by combining the articulation angle information. Finally, a simulation scene is built in Prescan to verify the stitching effect. The simulation results show that the proposed panoramic view system can effectively and clearly generate panoramic view images of semi-trailer trains at different articulation angles, effectively eliminating blind spots in their field of view and improving vehicle safety.
Towards a phenomenological understanding of neural networks: data

Abstract A theory of neural networks (NNs) built upon collective variables would provide scientists with the tools to better understand the learning process at every stage. In this work, we introduce two such variables, the entropy and the trace of the empirical neural tangent kernel (NTK) built on the training data passed to the model. We empirically analyze the NN performance in the context of these variables and find that there exists correlation between the starting entropy, the trace of the NTK, and the generalization of the model computed after training is complete. This framework is then applied to the problem of optimal data selection for the training of NNs. To this end, random network distillation (RND) is used as a means of selecting training data which is then compared with random selection of data. It is shown that not only does RND select data-sets capable of outperforming random selection, but that the collective variables associated with the RND data-sets are larger than those of the randomly selected sets. The results of this investigation provide a stable ground from which the selection of data for NN training can be driven by this phenomenological framework.
Visual transformer-based image retrieval with multiple loss fusion

Through hash learning, the image retrieval based on deep hash algorithm encodes the image into a fixed length hash code for fast retrieval and matching. However, previous deep hash retrieval models based on convolutional neural networks extract local information of the image using pooling and convolution technology, which requires deeper networks to obtain long distance dependency, leading to high complexity and computation. In this paper, we propose a visual Transformer model based on self-attention to learn long dependencies of images and enhance the extraction ability of image features. Furthermore, a loss function with multiple loss fusion is proposed, which combines hash contrastive loss, classification loss, and quantization loss, to fully utilize image label information to improve the quality of hash coding by learning more potential semantic information. Experimental results demonstrate the superior performance of the proposed method over multiple classical deep hash retrieval methods based on CNN and two transformer-based hash retrieval methods, on two different datasets and different lengths of hash code.
Helmet wearing detection method based on lightweight YOLOv5

A lightweight detection algorithm based on YOLOv5 is proposed to solve the problems that the target detection algorithm has many network parameters and the large size of the model is not conducive to the actual deployment. Firstly, the depth separable convolution was introduced to reconstruct the backbone network to reduce the network parameters. Then, the residual element was redesigned by ghost convolution and integrated into the Neck end to further reduce the model volume. Finally, the detection accuracy and recognition effect of the algorithm were improved by modifying the detection layer. When the recognition accuracy of the improved lightweight algorithm is reduced by 3.4%, the model volume is reduced by 75%, and the number of network parameters is reduced by 81%. The results show that the algorithm is able to maintain a high recognition accuracy, and the volume and parameters are greatly reduced. It is suitable for embedded and other system resource limited scenarios, and has a practical application prospect.
BERTベース分類器とSHAPを用いた地方議会議員の議会発言における特徴分析

Local assembly minutes record all the statements made in assemblies. Studies have been conducted to analyze local issues and to clarify the efforts and political stance of the assembly members based on the statements in the minutes. Previous studies have used word-based methods such as TF-IDF, which cannot consider multi-word phrases or contexts. In this paper, we construct a BERT-based classifier that predicts the speaker of each statement in the minutes, and calculate the contribution of each token to the prediction by SHAP. We then extract characteristic expressions of political interest from the statements in the minutes on a clause-by-clause basis according to the SHAP values, and analyze the experimental results. By taking into account the dependency relations between the clauses, the context of the extracted expressions can be presented. The analysis results show that our method can extract more characteristic expressions that reveal the political interests of the speaker than TF-IDF. In addition, we confirmed that this method can extract the speaker’s unique phrases and habits of saying, which are difficult to extract with TF-IDF.
LOF-enhanced SMOTE algorithm for imbalanced dataset

This paper proposes a new algorithm, LOF-Enhanced SMOTE, aimed at addressing the problem of imbalanced datasets in machine learning tasks. Due to the significantly fewer samples of certain classes in imbalanced datasets, the performance of classifiers may be negatively affected. To solve this problem, we introduce the Local Outlier Factor (LOF) algorithm to remove boundary noise on the basis of the SMOTE algorithm, and use a Gaussian kernel function to consider the similarity of generated samples. We conduct experiments on real intrusion detection data, UNSW-NB15. The results show that LOF-Enhanced SMOTE outperforms SMOTE and Borderline-SMOTE algorithms overall, and significantly outperforms them in detecting certain minority classes. This indicates that the LOF-Enhanced SMOTE algorithm can effectively solve the classification problem of imbalanced datasets.
Human motion recognition of active contrast coding model based on self-attention

The demand for high-precision human motion recognition continues to rise. In this experiment, an active contrast coding model based on self-attention is constructed. In the self attention based active contrast coding model, the model fully takes into account the feature differences of different properties of actions, and uses a fixed sliding window to segment the human activity data of inertial sensors with different properties. Cross-validation is used to improve the generalization ability of the model and prevent the occurrence of overfitting. The generalization ability and overfitting phenomenon of the model are evaluated by using the confusion matrix index, ROC curve when each movement is positive and visualization of the model. The accuracy of the final model is 0.987, the F1 score is 0.998, and the precision is 0.987; the recall rate was 0.976, and the AUC values of ROC curves with positive movements were greater than 0.97. Through the visualization of the model, it is found that the boundary between the A3 motion and A8 motion is not clear enough, and the boundary between A18 and A15 motion is not clear enough. Perhaps increasing the amount of data will lead to a more accurate classification of sports.
Research based on improved YOLOv5 algorithm in unmanned driving technology

With the rapid development of modern society, people in order to meet their travel needs, the demand for cars increased rapidly. In recent years, traffic accidents have occurred frequently, accounting for the highest death toll in China's workplace safety. According to statistics, in our country, the average number of people who died in traffic accidents always keeps at about one hundred thousand, countless families bear the pain and pain of parting loved ones. Road traffic accidents have become an extremely prominent problem that must be faced squarely. Ninety-three percent of road traffic accidents are caused by improper operation and immature driving technology. To solve this problem, in recent years, more and more scholars began to engage in the research of unmanned driving technology. This can greatly improve the safety of car driving, so as to reduce the occurrence of traffic accidents to a certain extent, to avoid the occurrence of tragedy. This paper mainly describes and improves the target detection technology algorithm in unmanned driving, and proposes an improved NAM-SIOU yolov5 algorithm based on the yolov5 algorithm. The original yolov5 algorithm incorporates the NAM attention mechanism and uses a better SIOU loss function. The experimental results show that the mAP and accuracy rate of the improved algorithm is improved by about 1 percentage point, the accuracy is up to 94.6%, the recall rate is up to 94.2%, the detection time of single vehicle pedestrian road image on GPU is 15.2ms, the detection speed is increased by about 23% compared with the original algorithm, and the real-time detection speed is up to 43 frames/s. The NAM-SIOU yolov5 algorithm can well meet the requirements of high accuracy, fast speed, high real-time performance and other indicators in unmanned vehicle technology target detection.
Community smells in software engineering: A systematic literature review

As a relatively new research field, community smells have received a lot of attention in recent years. This study aims to identify, evaluate, and synthesize previous works on community smells using the Systematic Literature Review (SLR) Protocol. Initially, a total of 125 research papers were retrieved from three electronic databases based on our defined search string. 21 papers were finally selected based on the selection criteria to be synthesized and analyzed in detail. After analyzing the documents, the research trends and approaches adopted in community smell research are discussed and presented Besides, the gaps in this domain have been identified. We concluded that more studies need to be done in this specific area to address the gaps.
On the method of address offset processing

To improve the accuracy of geocoding system in processing offset in fuzzy address. This paper presents a method of offset processing in fuzzy address. First, obtain the offset azimuth and offset distance according to the offset in the fuzzy address, obtain the road information around the offset point. Select an appropriate road to offset according to the road direction, and obtain the returned address data.
Sensor-based gesture recognition with convolutional neural networks

Recently, sensor-based gesture recognition has shown its importance through its various practical uses in the fields of control systems, medical monitoring and judging abnormal behaviors. Deep learning has been widely implemented in sensor-based gesture recognition problems, and the results prove its effectiveness. In this article, we propose a recognition model based on convolutional neural networks (CNNs) and apply it to smartphone-based data. The average classification accuracy is better than that of traditional machine learning models, which is 69.24% on this dataset. Furthermore, we analyze the effect of different factors on the result of the CNN model.