Reviewerly/exHarmony huggingface dataset:
A Target Tracking Scheme Design Based on Multiple Cameras

With the massive growth of video data, target tracking across multiple cameras has become one of the current research hotspots. The yolov5 algorithm has a wide range of applications in the field of target detection, with high performance and good flexibility. DeepSort and FastReID have good results in the field of multi-target tracking and pedestrian re-identification, respectively. In order to achieve target tracking under multiple cameras, we propose a multi-camera target tracking scheme based on the above three algorithms. It has been verified by experiments that the accuracy of this scheme is about 83.2%, and it has certain application value in the community environment.
Single-paragraph Extractive Machine Reading Comprehension Model Based on Multi-layer Aligned Attention

In this paper, a multilayer aligned attention reading comprehension model was proposed to address the attention deficit and redundancy problems of traditional multilayer attention-based extraction machine reading comprehension models. The model is designed with a three-layer aligned attention interaction layer in the question and context paragraph interaction layer, each layer consists of three parts in series: question and context alignment, context self-alignment, and context information inference. The model compensates for the information missing problem caused by the deeper network in the process of deep feature extraction by introducing layer interaction operations in each layer of question and context mutual alignment and context self-alignment, and using a gating mechanism for information fusion of multiple layers of attention. By extending the number of layers of attention interaction, the information interaction capability of the model is enhanced, while the slow update problem of deep network parameters is alleviated by the residual connection between each layer, which finally makes the problem of lack of attention and redundancy significantly improved and enhances the performance of the model. Finally, the validity of the model was verified using SQuAD[10], a large-scale reading comprehension dataset published by Rajpurkar at Stanford University.
Enhanced Two-stage Ship Detection Algorithm in Remote Sensing Image

Remote sensing images have the characteristics of extreme high resolution, small object and sparse distribution., which bring huge difficulties for ship detection in the sea. Traditional object detection models based on deep learning can not be directly applied to remote sensing images. This paper proposes an efficient ship detection framework based on Cascade R-CNN network. We use the YOLOv5 model as the first stage detection and simplify the Cascade R-CNN detector head. This new model is particularly suitable for detecting sparse objects in high resolution image.
Research on Key Technologies of Data Acquisition and Analysis of Integrated Energy Equipment Based on Power Internet of Things

In electric power construction promotion in the Internet of things, will reveal distributed power grid architecture design, a new type of composite, such as power electronic characteristics, will not only change the previous system structure, also puts forward the development direction of new, so we need personnel reasonable use of advanced technology such as artificial intelligence theory, a comprehensive optimization power iot systems. In order to better break through the difficulties faced by the development of the current power industry, it is necessary to fully integrate the power system and the traditional Internet of Things technology. Only in this way can we master the key technologies and solve the data acquisition problem of integrated energy equipment. On the basis of understanding the concept and technical framework of power Internet of Things, this paper clarified the architecture diagram and actual data collection method of multi-energy equipment data collection, analyzed the data collection and analysis performance of integrated energy equipment with power Internet of Things as the core, and comprehensively grasped the key technology of system operation.
Chinese Spelling Error Correction Based on Adaptive Pre-training and Multi-Task Learning

With the rapid development of computer application technology, more and more information is transmitted through the text through the network. The number of network texts grows fast, and it is difficult to guarantee the quality of the text. Low-quality information will pollute the network environment and spread error information. The traditional manual text check method has been unable to deal with today's massive data. Therefore, spelling error correction methods based on the deep learning model have attracted the attention of many experts and scholars in recent years. This paper proposes a Chinese spelling error correction model based on task adaptive pre-training and multi-task learning. Firstly, based on the Bert, this paper proposes an optimized mask strategy for the pre-training method to make the pre-training model better match the spelling error correction task. Secondly, based on the language model obtained by task adaptive pre-training, this paper combines spelling error detection and spelling error correction by multi-task learning, Finally, we test our model on the SIGHAN dataset, which is a general benchmark dataset of Chinese spelling error correction. Compared with other models, our model outperforms all competitor methods.
A Hybrid Recommendation Algorithm with Co-embedded Item Attributes and Ratings

In today's recommendation algorithm field, attribute feature information of users or items is usually introduced as auxiliary information to alleviate the sparsity problem, which can achieve good recommendation results to some extent. To make better use of the auxiliary information for data compression and dimensionality reduction, autoencoder models are usually employed. However, existing autoencoder-based models require the same input and output dimensions, resulting in data information loss and poor autoencoder scalability, which ultimately leads to low recommendation line performance. To cope with this problem, we propose a hybrid collaborative recommendation algorithm with co-embedded item attributes and ratings (HCR-item). By one-hot encoding the discrete item attribute features and then fusing the rating features with the item attribute features as the input of the autoencoder, the autoencoder completes the data compression and reconstructs the rating interaction prediction matrix. The algorithm proposed in this paper integrates item attribute features and rating features together, flexibly uses different feature information, extends the feature space, and solves the problem of low recommendation performance caused by sparsity. The algorithm has been extensively experimented on several real datasets and compared with other algorithms, and the results show that HCR-item has better recommendation performance.
Optimization of Recommendation Algorithm Based on Knowledge Graph

In the context of the new era, the network search engine technology can not only help users in the mass data information, quickly obtain the content of interest, but also gradually emerged more personalized recommendation system. By analyzing the application of personalized recommendation systems in recent years, it can be seen that common recommendation algorithms include collaborative filtering and content recommendation, etc. However, since they pay more attention to the scoring data of users' options and do not properly use the behavior sequence of multi-dimensional users, the actual recommendation accuracy is not high. Therefore, based on the integration of previous technical experience, this paper proposes a behavior path collaborative filtering recommendation algorithm with knowledge graph as the core. On the one hand, it needs to integrate and understand user behavior data, and fully consider the behavior graph and related path created by behavior sequence. On the other hand, vectorization technology should be used to make the path of text type realize vectorization, and the similarity between multi-dimensional behavior path vectors should be calculated accurately, so as to implement filtering recommendation. The final results show that the algorithm proposed in this paper can carry out effective recommendation in multi-dimension, which can not only make full use of data information, but also show the diversity of recommendation algorithm, which is the focus of practice development.
Study on the UAV Trajectory Planning Route Based on the Particle Group Optimization Algorithm

In the development of modern society, unmanned aerial vehicle (UAV), as an important technical means of national reconnaissance and combat, must ensure the accuracy of the actual reconnaissance target. According to the current operation of UAVs, flight path planning for mission is an important technology for UAVs to realize autonomous flight and attack. Therefore, this paper proposes a path planning system based on particle swarm optimization (PSO) algorithm while studying the uav path planning problem. This method mainly uses particle swarm optimization algorithm, fully integrates the search algorithm and constraint conditions, and obtains the global optimal path on the basis of controlling the search scope. The final research results prove that the path planning based on particle swarm optimization algorithm can quickly complete the planning task and orderly meet the operational requirements. Therefore, the research on the path planning system based on particle swarm optimization algorithm has a positive impact on the development of modern UAV technology.
High Resolution Display Model of Wind Profile Radar Based on Multi-core Liquid State Machine

This paper introduces artificial intelligence method to solve the technical problems of low spatial and temporal resolution which can not quantify some important data and data display, in view of the limitations of the fixed boundary layer wind profile radar data processing system at Lanzhou Zhongchuan Airport. The mathematical model kernel of wind profile radar data processing system is redesigned, and the high resolution display model of Lanzhou Zhongchuan Airport wind profile radar based on multi-core liquid state machine has a good experience through the verification. It provides a good technical support for the identification and alarm of low altitude wind shear and down burst flow at Lanzhou Zhongchuan Airport.
Research on Aggregation Strategy of Federated Learning Parameters under Non-Independent and Identically Distributed Conditions

Federated learning is an emerging distributed machine learning framework that can build a shared model under the condition that the data of each participant is not available locally. Non-IID data is one of the fundamental challenges in the application of federated learning. The existing federated learning aggregation algorithm FedAvg (Federated Averaging) will cause model accuracy and efficiency reduction in case of non-IID data. This paper proposes the grouped federated averaging algorithm GFedAvg (Grouping federated averaging), which improves the FedAvg method. The experimental result shows that the GFedAvg algorithm improves the model accuracy by 10% on average and reduces the running time by 50% compared with the FedAvg algorithm under the same conditions.
Analyzing the Fitting Effect of Approximate Functions in Homomorphic Encryption Process Under Cloud Computing

In order to ensure the security of machine learning data in the process of cloud computing, we use Taylor's theorem and least square method to approximate the Sigmoid function involved in the privacy protection logistic regression algorithm, and then analyze the fitting effect of approximate functions. The results show that he Taylor theorem is better than the least square method in fitting sigmoid function.
Text Mining Based on Web Crawler Technology to Analyze the “Reacting Force” of Film Reviews on Films

In this paper, the web crawler technology to dig out the factors that influence the development direction of the film by the public. Through experimental analysis and comparison, it can be concluded that in the modern society, the online film review not only fully reflects the guiding ideology of the audience in choosing films, but also the popular film and television culture has a potential impact on the development direction of the film of the times., and has a non negligible reverse thrust on the development of the film.
Implementation of Automatic Keyword Extraction Software for English Text Based on Python

In order to extract keywords from English Texts better, combined with the characteristics of English grammar, the software introduces two methods of document preprocessing: part of speech tagging and Morphological reduction. Before extracting keywords, it divides words into parts of speech, and mark words as nouns, verbs, etc first, then changes Morphological, uses TF-IDF, LDA and LSI algorithms to extracts keywords.The experimental results show that this method can improve the extraction effect.
Filter Pruning via Eigenfilter for Deep Convolutional Neural Networks

The sensing capabilities and pervasive networking of devices is driving the rapid growth of the Internet-of-Things (IoT), where collected data can be analyzed to uncover hidden patterns, correlations, and other insights. Despite the great potential of deep convolutional neural networks (CNNs) in many application scenarios, the prohibitively expensive computational costs make them difficult to be deployed on resource-constrained devices, especially mobile devices. Recent efforts toward reducing the computational costs involve filter pruning without hurting the original accuracy. However, existing methods perform filter pruning by evaluating the importance independently, rather than considering their importance collaboratively. To address this issue, in this paper we propose a new concept of eigenfilter to best approximate all filters in each convolutional layer. And we develop a filter-level pruning method, named Filter Pruning via Eigenfilter (FPE), which reduces the model size and accelerate the inference procedure by considering mutual relations of the filters within the same layer. Validation experiments on the MNIST and CIFAR demonstrates the compression effect of the FPE method. Compared with existing filter pruning strategies, our FPE strategy obtains state-of-the-art performance: trained on the CIFAR-10 dataset, the FPE-pruned ResNet-56 model reduces FLOPs by more than 54%, with only a drop in accuracy 0.76%; the FPE-pruned VGGNet-16 achieves 83.3% speedup and only 0.2% accuracy drop. Moreover, on CIFAR-100, FPE reduces 54.20% FLOPs on ResNet-50 and the accuracy drops by 1.04%.
The Current Situation and Prospect of the Development of Metaverse Technology

Metaverse uses technology such as VR, AR and other technologies to realize the transition from the real world to the virtual world mapping, using the blockchain Lightning Network and NFT unique authentication to decentralize management and authentication of assets in the metaverse. This paper summarizes the development of the metaverse and determines the structural framework of the metaverse, analyzes the relationship and connection between the metaverse and the extended reality (XR) and digital twins, and realizes the advantages of looking forward to the future development of the metaverse based on current technology.
Improved Pre-training and Semi-supervised Learning for Domain-specific Chinese Named Entity Recognition

With the explosive growth of information, automated text mining and knowledge discovery have become a hot spot in the research of Artificial Intelligence (AI). Named Entity Recognition (NER) is an important AI technology that needs to extract important entity information from the corresponding text. Today, most of the current NER technologies are based on Chinese characters. These methods, however, greatly ignores the vocabulary information in Chinese. How to use the rich vocabulary information in Chinese to enhance the effect of named entity recognition in the specific domain is still a challenge. What's more, in different domain, the labeled corpus is scarce, and labeling is expensive. Based on the pre-training model BERT and a large number of unsupervised corpus that are easy to obtain in the domain, this paper proposes an improved pre-training method of whole word mask of unsupervised corpus in the domain, and fully uses the unsupervised corpus in the fine-tuning task stage for semi-supervised learning. The experiment is carried out on the datasets in the domains of law and traditional Chinese medicine. The results show that the performance of the model method proposed in this paper in the domain dataset greatly exceeds that of the baseline model.
Non-Fungible Token forecast based on LSTM

As Bored Ape Yacht Club attracts more and more attention, the non-financially token based on blockchain technology also attracts our attention. Based on MetaWorld data in NFT as an example, this paper uses MetaWorld as prediction data to predict the trend of heterogeneous tokens. However, due to the short development time of NFT and its strong time influence factors, we introduce Long-Short Term Memory to predict the transaction data in NFT. By comparing the prediction with the real data, we found that the error between them is small and the goodness of fit can reach 0.988. Therefore, LSTM can be used to predict the trading trend of the meta-world in NFT.
A Boundary Detection Enhanced Model for People Name Recognition in Ancient Chinese Literature

Named entity recognition is a fundamental task in natural language processing, which aims to identify potential entities such as person, place, and organization in the text. Identifying names in ancient Chinese literature is helpful to discover Chinese traditional culture and promote traditional spirit. Unfortunately, there are two main problems for named entity recognition task in the field of ancient Chinese literature: (1) A scarcity of available annotated corpus has led to little research in this area. (2) Most existing work only focus on character embedding, resulting in limited performance. This is because character vector is difficult to consider the relevance of characters and words when processing Chinese texts, especially ancient Chinese texts. To tackle the above problems, we first introduce the distant supervision method to construct the required annotated dataset, and then propose a boundary detection enhanced named entity recognition model based on BERT+CRF. The proposed framework is proved to be effective through comparative experiments and achieve the best F1 value of 81.24%.
Evolution Analysis of Network Public Opinion Theme Based on LDA Model

Online public opinion is a collection of netizens' emotions, attitudes, opinions, opinions and so on. With the development of the Internet, the influence of online public opinion on social stability is increasing day by day. This paper takes the “COVID-19” event as an example, crawls the relevant news and comment data released by People's Daily, and firstly divides public opinion events into four stages according to the news popularity and life cycle theory: Tf-idf algorithm is used to strengthen the selection of key feature words in the corpus. Finally, LDA theme model is used to identify the topic of public opinion and mine the evolution law of network public opinion, which is helpful to effectively guide and control network public opinion and plays an important role in social stability.
Target Recognition Method Based on Incremental Learning

The traditional batch learning method is only suitable for the solidification recognition model with a fixed number of samples. When the number of new training samples is increasing, the existing and new training samples must be reintegrated, and the recognition model must be retrained to complete the model update. The time cost, computational cost and storage resources of each update are very high, which greatly reduces the efficiency of recognition. In this paper, a new target recognition algorithm based on incremental learning is proposed. By expanding the feature extraction network and classification network, the problem of catastrophic forgetting is effectively suppressed. At the same time, the decoupling small sample incremental learning model based on prototype registration uses meta-learning method in the training process to effectively improve the efficiency of model learning on small sample data. The results are verified on CIFA100 and Tianzhi Cup challenge data sets. After two incremental learning, the recognition accuracy of the algorithm is reduced by only 21 % and 13 %, which is higher than the existing best algorithm by 4.7 % / 19.6 %. The results show that the method based on sample playback and model expansion is compatible, which can improve the accuracy of existing methods.
Named Entity Recognition of Chinese Legal Text Based on BERT

With the deepening of national judicial reform, how to combine artificial intelligence with judicial work has become a research emphasis in judicial intelligence research. Aiming at polysemy problem in Chinese, and characteristics of Chinese legal text that complicated context, professional, and diverse types of entities, we design a named entity recognition method for Chinese legal text based on BERT. Firstly, we build a Chinese legal text corpus, and utilize the corpus to domain pre-train the pre-training model BERT to make it perform better for named entity recognition task in Chinese legal text. Secondly, we design the method of adding clue words and the method of replacing synonyms for training data augmentation to increase the diversity of training data. Finally, we combine the BERT model after domain pre-training with a CRF layer, and utilize the datasets after data augmentation to train it. Meanwhile, we utilize adversarial training in the training process to improve the generalization ability of the model. To verify the performance of our model, we conduct experiments on the competition data set of the information extraction track of the China Legal Intelligence Technology Evaluation Competition, which proves the feasibility and effectiveness of the method.
A Multiattribute Decision Model to Evaluate Potential Investments in Near-Earth Object Detection Technologies

Asteroids and other near-earth objects (NEOs) pose a significant ongoing threat to our planet, with the potential to catastrophically disrupt life on Earth. Advance detection is essential to be able to respond to any object on a collision course, but detection and tracking technologies require substantial financial commitments. In this paper, we provide a multiattribute utility framework to analyze whether and which NEO detection technologies are worthwhile. This framework enables rigorous and systematic understanding of the uncertainties, multiple objectives, and tradeoffs inherent to advance decisions involving low-probability, high-consequence events. Using some reasonable baseline parameter estimates, the model shows that the detection technology investment decision is driven more by the abundant population of small (<140m) undiscovered NEOs than any other size group. We subsequently extend the framework to consider how a decision maker might evaluate alternatives when the risk affects a larger group of people, but the economic cost of investment is shouldered by a smaller subset of stakeholders.