Reviewerly/exHarmony huggingface dataset:
Storm Streaming Technology Applied to Intelligent Analysis of Meteorological Data in the Era of Big Data

With the continuous improvement and rapid growth of meteorological service informatization, it is very necessary to apply real-time meteorological data (MD) processing to meteorological information business. In the era of big data, it is a subject of great significance to obtain meteorological observation data in real time to provide meteorological researchers with knowledge decision-making based on MD and predict future meteorological changes. Based on this, this paper applies the modern storm streaming technology (MT) to the intelligent analysis of MD, and proposes a BP Neural network algorithm (NNA); The advantages of storm flow technology in rainfall prediction model and the pretreatment process of meteorological observation data are analyzed; The application of storm flow technology in intelligent analysis of MD is discussed; Through the learning results of neural networks with different numbers of hidden neurons and the formal prediction and verification analysis of MD, the results show that the application of storm MT to the intelligent analysis of MD has good performance in accuracy and stability. Compared with other model prediction data, the BPF model prediction data generated in the learning process also has good stability, and its error has been effectively reduced. In this paper, storm flow technology is applied to the intelligent analysis of MD, which greatly improves the accuracy of MD analysis.
The Design and Implementation of Integrated Network Teaching Platform Based on Java

With the rise of the Internet in China and the penetration of mobile phone ports and computer ports to Chinese users, this system has the premise and foundation. In order to solve the current problems of the epidemic, so that students can learn the knowledge normally and even exceed the quality of teaching under the normal mode, this comprehensive online teaching platform is hereby established. The design of the B/S structure makes the platform run perfectly, the application of JSP technology solves the problem of dynamic pages, and the background is the commonly used and mature MYSQL database technology. Breaking the traditional teaching model allows students to learn knowledge at any suitable time and place, and master high-quality resources from the Internet. Students registered on the platform of this system can purchase courses online and view their studies in My Center, and the administrator will perform background management of the member students.
Pelatihan Mendeley Bagi Mahasiswa Magister Manajemen Universitas Gorontalo

The use of reference manager software in writing a scientific paper is an obligation for students of the Postgraduate Program at the University of Gorontalo. Unfortunately, there are still many masters of management students who don't know how to use the application at all. The purpose of this activity is to train students to get used to using the Mendeley application in writing a scientific paper. The method used is by conducting a direct introduction and training on how to use Mendeley. The results of this activity are (1) Students get new information and knowledge about reference manager software and citation techniques. (2) Students can apply the use of Mendeley software when compiling scientific work.
Blockchain for Supply Chain Data Security Sharing Consensus Algorithm Design

The centralized data storage mode adopted by the traditional supply chain management system has some problems, such as single point of failure, data privacy disclosure, opaque internal operation of the system and so on, which seriously restricts the information flow and data sharing among enterprises. Blockchain is distributed, open, transparent and tamper proof. It can provide reliable underlying services for the implementation of distributed data security sharing system. Therefore, this paper proposes a blockchain based supply chain quality data security sharing model, which takes the distributed blockchain network as the core to build decentralized data security sharing services. At the same time, the practical Byzantine fault-tolerant (pbft) algorithm used in blockchain has some problems, such as large consensus delay, low throughput, poor performance, and does not support node dynamic management. Combined with the characteristics of supply chain alliance chain, by introducing simplified consistency protocol and new node management mechanism, the communication complexity of the algorithm is reduced and the dynamic management of nodes is realized.
Research on I-PageRank algorithm model of Process knowledge graph based on K-Shell decomposition algorithm

PageRank algorithm in the calculation of nodes is equally distributed to the node chain of all nodes, but in the actual production of manufacturing enterprises, the importance of process knowledge in process documents is different, if according to the PageRank algorithm PR value equal transfer to calculate the importance of the artifact, efficiency and accuracy is generally low, so the importance of PR value transfer difference should be considered. Therefore, this paper introduces K-Shell decomposition algorithm in PageRank algorithm, constructs a new I-PageRank algorithm model, adding the importance of each node in the linked network to the PageRank algorithm, which improves the efficiency and accuracy of PageRank algorithm in identifying key nodes.
Design and Optimization of Loss Functions in Fine-grained Sentiment and Emotion Analysis

The unbalanced distribution of category labels and the correlation between these labels tend to cause over-learning issues in deep learning models. In fine-grained sentiment analysis datasets, the correlation between category labels and the heterogeneity of tag distribution are prominent. In the deep learning model, we use the adjusted circle-loss to introduce margin and gradient attenuation in the loss function to handle the challenges caused by unbalanced label distribution and non-independence between labels. This method can be well combined with pre-trained models and adapt to various learning models and algorithms. Compared with the state-of-the-art typical models, our loss function mechanism achieves significant improvement using SemEval18 and GoeEmotions by measure of Jaccard coefficient, micro-F1, and macro-F1. It implies that our solution could work efficiently for sentiment analysis and sentiment analysis tasks.
Implementation of License Plate Detection Based on Improved YOLOv5s

In order to solve the problem of low accuracy of license plate detection, an improved license plate detection algorithm is proposed. The super-resolution reconstruction network SRGAN is used to enhance the image of the dataset and make the image of the license plate area clearer; The fourth C3 module of YOLOv5s backbone network is replaced with CBAM attention mechanism module to enhance the ability of backbone network to extract feature information, thus improving the detection accuracy. The experimental results show that YOLOv5s network using SRGAN for image enhancement and embedding CBAM attention mechanism improves the accuracy of license plate image.
Named Entity Recognition Method Based on BERT-whitening and Dynamic Fusion Model

In the context of Natural Language Processing (NLP), Named Entity Recognition (NER) plays a crucial role in tasks like entity relationship extraction and knowledge graph construction. The accuracy of Chinese NER heavily relies on the representation of word embeddings. However, traditional word representation methods like word2vec suffer from word ambiguity and singular word vectors. Similarly, BERT-based word embeddings also exhibit anisotropy. To tackle these challenges, we propose a novel NER method that leverages BERT-whitening and dynamic fusion of BERT’s output from different layers. The dynamic fusion module calculates a weighted sum of BERT’s output across multiple layers, while the whitening module applies a whitening operation to eliminate the anisotropy of word embeddings. By integrating these modules, our model effectively captures the characteristics of input words, providing robust support for subsequent decoding. We evaluate our approach on the CLUENER2020 Chinese fine-grained named entity recognition dataset. Experimental results demonstrate that our method outperforms the traditional BERT-BiLSTM-CRF model without external resources and data expansion, leading to significant improvements in performance.
Graph-to-Text Generation Combining Directed and Undirected Structural Information in Knowledge Graphs*

Graph-to-text generation task is transforms knowledge graphs into natural language. In current research, pretrained language models(PLMs) have shown better performance than structured graph encoders in the generation task. Currently, PLMs serialise knowledge graphs mostly by transforming them into undirected graph structures. The advantage of an undirected graph structure is that it provides a more comprehensive representation of the information in knowledge graph, but it is difficult to capture the dependencies between entities, so the information represented may not be accurate. Therefore, We use four types of positional embedding to obtain both the directed and undirected structure of the knowledge graph, so that we can more fully represent the information in knowledge graph, and the dependencies between entities. We then add a semantic aggregation module to the Transformer layer of PLMs, which is used to obtain a more comprehensive representation of the information in knowledge graph, as well as to capture the dependencies between entities. Thus, our approach combines the advantages of both directed and undirected structural information. In addition, our new approach is more capable of capturing generic knowledge and can show better results with small samples of data.
Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.
Small Object Detection Based on Context Information and Attention Mechanism

In order to solve the problem of missed detection and false detection of small targets in object detection, and to improve the detection precision and recall of small object, this paper proposes a small object detection algorithm that introduces context information and attention mechanism. The algorithm is improved on the Faster RCNN network architecture, and a multilevel feature fusion module is proposed to solve the problem of incomplete extraction of detailed information. The proposed regional attention module solves the interference of background noise and focuses on the target to be detected. At the same time, in order to more effectively meet the characteristics of small target detection, we have improved the anchor box. The method proposed in this paper is verified on DIOR, PASCAL VOC2007 and MS COCO datasets. Experiments show that the algorithm proposed in this paper and the current advanced algorithm have better accuracy and precision in detecting small targets.
Data-Driven Pruning Algorithm Based on Result Orientation

With their outstanding performance in natural language processing tasks such as machine translation and semantic recognition, deep neural networks have attracted great attention from both academia and industry. For more complex NLP tasks, people try to add more parameters to the network, expand more layers, input larger data samples, to produce a large model to solve the complex task. However, it is not the case that the deeper the layers, the better the parameters. There is a large amount of redundant information in the parameters, which not only contributes nothing to the results, but also increases the computational burden of the model and the storage burden of the hardware. Eliminating a small amount of redundant information often has no effect on the recognition rate of the model, but slightly improves it [1], so the neural network model needs to be compressed. Existing compression methods include model pruning, parameter quantization, tensor decomposition, knowledge distmation, etc. [2] In this paper, model pruning algorithm is selected to implement a result-oriented data-driven pruning algorithm by introducing the propagation characteristics and inter-layer correlation of neural networks and automatic decision making based on Feature Map information. Finally, the effectiveness of the result - oriented data - driven pruning algorithm is proved by comparative experiments.
CON-GAN-BERT: combining Contrastive Learning with Generative Adversarial Nets for Few-Shot Sentiment Classification

Sentiment classification is a classical and important task of natural language processing (NLP), with the development of the Internet, there are multifarious reviews, comments and news produced everyday which need high cost to annotate, so it has become a challenge to develop a more effective sentiment classification model which requires less training samples. In this paper, we propose a sentence level sentiment classification model based on Contrastive Learning, Generative Adversarial Network and BERT (CON-GAN-BERT). Experiments on several public Chinese sentiment classification datasets show that CON-GAN-BERT significantly outperforms strong pre-training baseline, and still obtaining good performances for Few-Shot Learning without any data augmentation or unlabeled data.
Chinese Semantic Role Labeling Based on BILSTM-CRF Extended Model

Semantic role labeling (SRL) is a technique to analyze the structure of predicates and thesis elements in a sentence as a unit. It plays an important role in Chinese information recognition processing. Among the models of SRL studied in recent years, most of them are based on bidirectional long and short term memory loop network and conditional random field. In this paper, we first narrate the SRL model based on BILSTM-CRF, based on which the second model narrates the SRL model integrating Bert and BILSTM-CRF models due to the ability of pre-training and fine-tuning of Bert model. However, since the word vectors in Chinese text are obtained based on word stitching in the context window, making the words between them influence each other, the word vectors depend on this joint relationship. Therefore, for this, Gate filtering mechanism is integrated to adjust it, and in the third model, Gate mechanism is added to filter and denoise the word vectors based on BILSTM-CRF to further improve the recognition ability of SRL.
N-ary Relational Link Prediction Algorithm Fusing Graph Attributes

Knowledge graph is widely used in real life, but there is still a lot of missing information, which makes the completion of knowledge graph very important. Link prediction is one of the main methods to complete knowledge graph. In addition to binary relation facts which have received a lot of attention, there are also hyper-relation facts that are ubiquitous in the real world, namely n-ary relation facts. In this paper, we focus on link prediction algorithms for n-ary relation facts and find that the existing algorithms ignore the graph attribute information of nary relation facts themselves in the calculation process. Consequently, the distribution of entities and relations in n-ary relational datasets is analyzed first. The results show the fact that some of the n-ary relation facts are very important, while others are less important. This indicates that they have the characteristics of the scale-free network. Then, the global graph parameter (GGP) is introduced to describe the importance of entities and relations, and weighted to the link prediction process to improve the accuracy performance. Finally, extensive evaluation on commonly used n-ary datasets JF17K, WikiPeople, and their specific arity subsets validate the superiority of the proposed algorithm.
Siamese Network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese Network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network’s ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%; On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements.
A Framework for Early Detection of Cyberbullying in Chinese-English Code-Mixed Social Media Text Using Natural Language Processing and Machine Learning

This study develops a new expert system framework to address the issue of early detection of cyberbullying incidents in Chinese-English code-mixed language on social media networks. The framework covers the crawling of session-based social media texts with potential cyberbullying messages with a crowdsourcing web application to systematically retrieve and manually annotate a cyberbullying dataset, and most importantly establishes an explainable artificial intelligence model based on natural language processing algorithm for identification of targeted emotional colloquial slang phrases and machine learning method using Shapley value and transfer learning approach for automatic early detection of cyberbullying incidents in Chinese-English codemixed language.
Multimodal Visual Question Answering Model Enhanced with Image Emotional Information

Visual Question Answering is a multimedia understanding task that gives an image and natural language questions related to its content and allows the computer to answer them correctly. The early visual question answering models often ignore the emotional information in the image, resulting in insufficient performance in answering emotional-related questions; on the other hand, the existing visual question answering models that integrate emotional information do not make full use of the key areas of the image and text keywords, and do not understand fine-grained questions deeply enough, resulting in low accuracy. In order to fully integrate image emotional information into the visual question answering model and enhance the ability of the model to answer questions, a multimodal visual question answering model (IEMVQA) enhanced by image emotional information is proposed, and experiments are carried out on the visual question answering benchmark dataset. The final experiment shows that the IEMVQA model performs better than other comparison methods in comprehensive indicators, and verifies the effectiveness of using emotional information to assist visual question answering model.
Post-encoding and contrastive learning method for response selection task

Retrieval-based dialogue systems have achieved great performance improvements after the raise of pre-trained language models and Transformer mechanisms. In the process of context and response selection, the pre-trained language model can capture the relationship between texts, but current existing methods don’t consider the order of sentences and the relationship between the context and the response. At the same time, as the problem of a small number of positive samples in retrieval-based dialogue systems, it is difficult to train a learning model with high performance. In addition, existing methods usually requires the larger computational cost after splicing the context and the response. To solve the above problems, we propose a post-encoding approach combining with the strategy of contrastive learning. The order of the context and the relationship between sentences in dialogues and response are reflected in the encoding process, and a new loss function is designed for contrastive learning. The propose approach is validated through experiments on public datasets. The experiment results show that our model achieves better performance and effectiveness compared to existing methods.
CARMEN: A Method for Automatic Evaluation of Poems

Automatic poem generation has been a challenging topic in Natural Language Processing research. However, their evaluation is still largely based on methods that involve evaluation by human judges, comparing the results with poems written by humans, or using metrics that were not designed for this purpose. In order to fill this gap, this work proposes a specific metric for automatic evaluation of poems, capable of quantitatively evaluating morphological characteristics of the text, such as rhyme and meter, of different types within this literary genre. The tests carried out demonstrate that the metric developed presents an advance for the evaluation of this type of text.
A two-stage e-commerce search matching model incorporating contrastive learning and course-based hard negative example sampling

Text matching is a fundamental task in natural language processing. To address the short and ambiguous search statements in e-commerce domain, the complexity of headlines and the expensive manual annotation samples, this paper proposes a two-stage "vectorized retrieval + refined ranking" text matching model with a mixture of contrastive learning and course-based hard negative example sampling. By using supervised learning data augmentation, domain pre-training, comparative learning and hard case sampling to assist in ranking, this work achieves an MRR@10 value of 0.3890 in the test set of the 2022 "Ali Lingjie" E-Commerce Search Algorithm Competition, ranking second, demonstrating the effectiveness of the model.