Reviewerly/exHarmony huggingface dataset:
Machine Vision Techniques for Cervical Intraepithelial Neoplasia Detection

Cervical intraepithelial neoplasia (CIN) is a precancerous condition of the cervix that, if left untreated, can develop into cervical cancer. Preventing cervical cancer requires the early detection and management of CIN. Machine vision techniques have the potential to greatly enhance CIN-C diagnosis and treatment. These methods can be used to examine digital images of cervical cells and determine whether any aberrant cells are present based on characteristics like size, shape, colour, and texture. Additionally, patterns in cervical cytology samples that are suggestive of CIN can be recognized by machine learning algorithms. The accuracy and specificity of CIN-C diagnosis can also be increased by combining machine vision with other technologies like optical coherence tomography and spectroscopy. Additionally, the development of portable and low-cost cervical cancer screening systems can increase access to healthcare in resource-limited settings. This paper reviews the current state of the art in machine vision techniques for CIN-C detection, highlighting recent developments and future perspectives in this field.
Reverse Engineering Piping and Instrumentation Drawings: A Deep Learning Approach

Data visualization is an easy way to communicate knowledge ever since human evolution. There are many visualization techniques including different types of plots, graphs, charts, and many more. Despite its usefulness, there is one common problem with visualization. The static image that generates the visualization is separated from the raw data. When a research study requires raw data it's hard to reverse engineer and take the raw data back. There are very limited tools available to reverse engineer and extract raw data from the above-mentioned static image. However, it's challenging to analyze all the data visualization techniques at a single glance. So, this research is focused on data extraction and summarization from Piping and Instrumental Drawings(P&ID). The dataset contains symbols taken from 2432 instances of piping and instrumental drawings. The goal of this study is to improve the accuracy of the prebuilt Convolutional Nural Network(CNN) model with hyperparameter optimization techniques and create a web application where users can extract and summarize text and symbols data from any piping and instrumentation drawing. As future directions, the applicability of other visualization techniques is also discussed at the conclusion.
Comparative Study on Extractive Summarization Using Sentence Ranking Algorithm and Text Ranking Algorithm

It is possible to create an accurate and succinct summary using a method called automatic text summarising. Before creating the necessary summary sentences, the machine learning algorithms may be trained to understand documents and recognise the portions that transmit crucial facts and information. Abstractive text summa- rization and Extractive text summarization are its two methods. An extractive text summarization refers to the process of removing significant text from a text document or source manuscript. Deep learning techniques are used to train an abstractive text summarizer, which then generates new phrases and keywords based on the content of the original text. The process of acquiring and assimilation of the knowledge from numerous sources takes time. Automatic text summary is used to speed up the process of finding and consuming pertinent information. An extractive text summarising technique chooses crucial phrases, sentences, and other parts of the original text and concatenates them into a compressed form. The extractive text summarization produces grammatically correct sentences. The project is to develop a model that demonstrates extractive text summarization using sentence ranking and text rank algorithm. It shows that the text rank algorithm does a remarkable performance to extractive text summarization. Using a sentence ranking algorithm, extractive summaries are created that rate sentences according to their weights. In order to create a high-quality summary of the input document and store the summary as audio, the input document's highly scored sentences are extracted. A non-supervised, extractive text summarising method is called TextRank. While calculating the value of words and phrases in a document, the TextRank algorithm looks at how they relate to one another. According on how important the words are in each phrase, it then assigns a score to each one. The summary is formed using the algorithm's choice of the most crucial phrases.
A blockchain and multi factor fusion based electronic identity registration and verification system

This work proposed a decentralized and multi factor fusion approach to electronic identity registration and verification, and uses blockchain technology to complete the design of the system. The system includes client, identity registration system, identity verification system, electronic signature system and blockchain service module, so as to complete the registration and verification service. The registration system can read citizen’s identity from the ID card by OCR or NFC, processed by electronic signature system, generate a QR code which contain the encrypted electronic identity. The consortium blockchain and decentralized identity storage system are used to store the encrypted identity information. The verification system use identity information plus biometric multi-factor fusion identity verification mode to create a unique ID and using location sensitive hash algorithm to process biometric features can identify a citizen. The system is efficient and trustworthy, with higher security than the previous centralized authentication scheme.
Combining i* and Service Blueprint for Service design:a case study of Fliggy

Requirements modeling and software services design are two important activities in software life cycle. Although these two activities are at different stages of the software life cycle, they need to be considered together. When the requirements are defined clearly, the next task is service design. In the process of performing service design tasks, it is necessary to consider meeting the complex needs of users. In this paper, a method which combines iStar and service blueprint is proposed to support the crossover services design. The iStar framework is used to capture user intensions and the service blueprint is used to model the interactions between different components. To evaluate the proposed method, a case study is performed to illustrate the usage of the proposed method.
Research on Chinese word sense disambigution based on Prompt learning

Word sense disambiguation is an important research topic in natural language processing. Its purpose is to determine the word meaning of a polysemous word in different contexts. In order to improve the efficiency of disambiguation, a Chinese word sense disambiguation model PL-WSD based on prompt learning is proposed. The model uses the knowledge base HowNet to obtain the representation of each word meaning in polysemous words, uses the way of prompt learning to calculate the similarity score of each word meaning in a specific context, and obtains the word meaning with the highest score as the final result of polysemous words. Experiments show that PL-WSD model is greatly improved compared with other models.
A Method for Building Chinese Domain Lexicon Based on New Words Recommendation

Professional domain lexicon construction is the basis for Domain-specific Natural Language Processing. Current new words detection algorithms struggle with migration and sparsity and are unable to facilitate the building of domain lexicons. In this paper, a method for building a domain-specific lexicon based on new words recommendation is proposed. A model of the domain lexicon construction process was developed, including domain corpus pre-processing, new words detection, saliency evaluation, and new words recommendation. The problem of the sparsity of unsupervised new words discovery algorithms is solved by using Enhanced Mutual Information and Branch Entropy to identify small word frequency words and NC-value to identify domain words. According to experiments on a guided domain dataset, the method is effective at enhancing the precision of domain new words recommendations, lowering the complexity of expert work, and has significant implications for the building of a domain-specific lexicon.
Analysis of Real-time Image Target Detection and Recognition Algorithm Based on Video Stream

In order to further strengthen the management and control of information maintenance operations, a real-time image recognition algorithm based on video stream is proposed. By establishing an information maintenance business risk analysis model, the operation and maintenance behavior and input/output flow information of the information system can be automatically identified, and the operation and maintenance process can be analyzed in real time. Command operation, log exception analysis, personnel abnormal behavior, work order text and other information can quickly locate abnormal operations and alert operational risks, realize unified monitoring, continuous positioning and risk warning of remote operation and maintenance of the information system, aiming to enhance the life cycle management and control capabilities of the overall information maintenance system, reduce maintenance risks, and improve the quality and efficiency of information operation.
Design and implementation of a traditional martial arts follow-up training system based on virtual reality technology

With the popularity of the metaverse concept, virtual reality technology is increasingly being applied to all aspects of life, and its rapid development has far-reaching implications for the promotion of non-heritage culture. The movements of the inheritors are captured and given to the virtual characters; users who follow the movements of the virtual characters in the VR helmet will get an immersive sense of practice. This paper takes the background of traditional martial arts follow-up training and the dissemination of non-heritage culture, based on Unity3D software, integrates more than ten traditional martial arts, boxing methods and dances, and develops a traditional martial arts follow-up training system that is conducive to improving learning concentration and learning efficiency, in an attempt to create a Metaverse of non-heritage cultural forms, so that more people can gain a deeper understanding of traditional martial arts and obtain a better follow-up training effect.
Knowledge graph question answering system based on code defect

In the current scenario of code defect aided search, the code defect knowledge is not accurate enough due to the singularity and island of code defect knowledge, so that developers and testers can not accurately use the defect knowledge. To solve this problem, this paper proposes a question answering system construction method based on code defect knowledge graph. Firstly, a large number of code defect knowledge is cleaned to obtain the entities and relationships of code defect knowledge, and the corresponding code defect knowledge graph is established. Then the combination of Bert model and Prompt makes the system better identify user intentions. The effectiveness of this method is verified based on the accuracy comparison with the commonly used models in this field. Experiments show that the combination of Bert model and Prompt makes this method more accurate in identifying user intention.
Gazetteer Enhanced Named Entity Recognition for Musical User-Generated Content

The vast amount of social information distributed over various electronic media channels, namely User-Generated Content (UGC), has been the fastest growing source of information in recent years. Named Entity Recognition (NER) researches on UGC in specific business areas such as music have potential widespread application prospects, such as user intention analysis and products recommendation. Current neural NER models achieve remarkable performance on standard benchmark tasks. It is challenging to deal with noisy, low-context text and complex, emerging entities in real-world setting. A new approach has been proposed to solve the issues. It learns character-level and token-level knowledge simultaneously, and incorporates gazetteers as external knowledge with them which augment the understanding of the music UGC. Experimental results show that our approach achieves an improvement of 15 points in F1 score compared with the modern baseline.
Multi-target tracking random finite set model based on signal parameter correlation

The target is generally equipped with a corresponding radiation source, and the signal parameter of the radiation source can be used as an important auxiliary parameter to improve the tracking performance of the electronic reconnaissance system to the target. For the practical application scenario of multi-tracking, we proposes an improved Gaussian mixture probability hypothesis density filter (GMPHD) filtering algorithm based on the correlation of radiation source signal parameters. By using the signal parameters to expand the dimension of the target’s motion state vector, the description of the target state is improved. Based on the statistical theory of random finite sets, the multi-target state is described as a random finite set, and GMPHD is used for tracking. Simulation results show that the use of signal parameters can enhance the precision of data correlation, thereby improving the performance of the trace.
A Method for Counting Models on Cubic Boolean Formulas

We present an algorithm based on heuristic variable selection for computing the number of models on two conjunctive normal form Boolean formulas whose restricted graph is represented by a cubic graph. For this class of formulas, we show that in most of the cases our proposal improves the time-complexity with respect of the current leader algorithm for counting models on two conjunctive form formulas of this kind.
Personalized Course Recommendation Model Based on User’s Interests and Feature Information

To resolve the problems of sparse data and poor recommendation effect in course recommendation, a personalized course recommendation model based on user’s interests and feature information is presented in this study. Firstly, the weight of feature information of the course is defined to reflect the importance of the attributes of the course in the recommendation process. Secondly, the exponential decay function is designed and the time coefficient is integrated to fully adapt to the changes of user interests over time. Finally, the collaborative filtering recommendation scheme is introduced to integrate user interest preferences and attributes to obtain a comprehensive similarity. The experimental results show that the experimental results on the dataset show that the proposed collaborative filtering recommendation model can provide better recommendation quality than traditional algorithms.
Multi-center Federated Learning with Model Decoupling

Federated learning enables multi-participants to collaboratively train a single model to solve the problem of data silos. But federated learning faces the problem of data heterogeneity. In this paper, we propose a simple and efficient personalized federated learning framework named FedMDC to solve the data heterogeneity problem. Adhering to the idea of seeking common ground while reserving differences, we effectively combine the idea of clustering and model decoupling, and provide each participant with a personalized model suitable for their own local data distribution. Our extensive experiments show that FedMDC significantly outperforms the other advanced federated learning algorithms on various image classification tasks.
A Sentence Semantic Matching Model Based on Cross-Attention Mechanism

Most of the current deep learning based sentence semantic matching uses Siamese network to extract semantic features and then uses simple attention mechanism for interaction, which is effective for extracting coarse sentence semantics, but it is difficult to extract deep interaction matching information, and it is difficult to distinguish sentences with similar structures but different semantics. Therefore, a sentence semantic matching model based on cross-attention mechanism is proposed to build an interaction module with cross-attention mechanism to extract interaction matching information of sentences and learn richer interaction features of sentences; meanwhile, a Roformer-ft pretraining model is used to embed and encode sentences so that the model can learn fine-grained semantic differences of sentence pairs more effectively. It is demonstrated experimentally that the model in this paper exhibits better matching performance.
METHODOLOGY FOR DESIGNING THE TRAINING COURSE "EXCURSION GUIDE" IN THE SYSTEM OF VOCATIONAL EDUCATION

The article is devoted to the problem of professional training of guides in the system of secondary vocational education. The current state of the educational program for the training of guides is analyzed and the need for the development of a training course "Guided tours" according to the program of secondary vocational education is justified. The article considers the methodology of selecting the content and designing the course "Guided tours" based on modern concepts of the theory of teaching and upbringing in vocational education. The features of the organization of expert evaluation of the effectiveness of the introduction of the training course "Guided tours" are investigated. The organization of the educational environment of teaching the course "Guided tours" on the basis of the Museum of Military Glory of the RMAT is considered. The experience of students' educational and scientific creativity is considered through the design of excursion activities and the creation of a unique creative educational environment that meets the requirements of the practical orientation of the educational process through integration into the educational environment of the Museum of Military Glory.
Subgraph-aware virtual node matching Graph Attention Network for entity alignment

With the vigorous development of knowledge graph, its construction has become a hot issue recently. However, most knowledge graphs are incomplete, and it is worthwhile to devote much effort to further fuse the knowledge of different entities. Hence, entity alignment has surprisingly emerged as a key step of knowledge fusion to tackle the above issue. The existing GNN-based models simply aggregate entity embeddings, or incorporate auxiliary information of entities (e.g., relationship, time, etc.), which frustratingly fail to consider neighborhood structure of entities and the cross-graph matching information of the nodes between two knowledge graphs. Thus, to overcome the two challenges, we propose a novel Subgraph-aware Virtual Node Matching Graph Attention neTwork for entity alignment, named SVNM-GAT. Specifically, to strikingly capture the cross-graph matching interaction information of entities, we propose a new virtual node matching mechanism, which can obtain the long-range dependencies between virtual nodes and holistic nodes by scale-dot attention. Additionally, a subgraph discriminator is presented in SVNM-GAT to identify the subgraph structure and obtain weighted adjacent matrix by calculating subgraph structure coefficients. Finally, we adopt experiments to verify the effectiveness of our model on the cross-linguistic and temporal datasets, and all the results indicate that SVNM-GAT considerably outperforms the state-of-the-art methods.
Dynamic method to optimize memory space requirement in real-time video surveillance using convolution neural network

Real-time video surveillance is one of the most effective ways to observe crime, mischief, and violence. But, most of the recent surveillance system consumes huge memory space to store the video. This article proposed an advanced dynamic video surveillance strategy to utilize minimum memory space with the finest detection of suspicious moving objects. In the proposed system, a convolution neural network (CNN) classifier and frame resolution switching is used to detect the movement of objects with appropriate frame resolution. A high-definition (HD) frame can be recorded dynamically when any movement of a suspicious object is detected. The system records low-quality frames, which are less important. Finally, the improved gradient-based histogram equalization technique is applied to all frames to obtain enhanced suspicious frames. Several real-time imperial tests are conducted and it is observed that the proposed system detects suspicious objects with 98.25% accuracy. Besides, the system consumes 80% less memory storage.
Leukocyte Detection with Novel Fully Convolutional Network and a New Dataset of Blood Smear Complete Samples

The analysis of leukocytes in blood smear sample images has been a successful tool for medical diagnosis, and there are machine-learning methods for segmenting and classifying leukocytes with these images. However, the datasets for designing these methods have images with different compositions than blood smear samples acquired with the standard protocols used in real clinical laboratories. Then, to contribute to the effort to improve the research related to the analysis of leukocytes, this paper presents a new dataset and a method for Leukocyte detection named Color Normalized UNet (CUNet). LeukoSet comprises 1497 images of blood smear complete samples using a protocol developed in a real clinical laboratory environment. The dataset has ground truths that classify the leukocytes into Lymphocytes, Monocytes, Neutrophils, Basophils, Eosinophils, and deformed leukocytes. CUNet is a Fully Convolutional Network (FCN) based on UNet for leukocyte detection designed with the images of LeukoSet. Experiments report that CUNet has an accuracy of 94.22% and an inference time of 27 ms. The results of CUNet are better than other leukocyte detection methods implemented in the literature.
TsP-Tran: Two-Stage Pure Transformer for Multi-Label Image Retrieval

Image retrieval aims to find similar images given the query. Most of existing retrieval works are based on the pre-trained model of single-label image classification. In practice, the query usually contains more than one instance, and the single label is far from enough for fully depicting the attributes of an open-world image. Due to the complicated similarity relationships between multiple semantics, the multi-label image retrieval task is not so well solved as the single-label task. In this work, we propose a two-stage pure Transformer model for multi-label image retrieval, which leverages a Transformer encoder to exploit the complex dependencies among visual features and labels. Except for the Transformer encoder, the image feature embedding module is also based on Transformer, so that the optimal model weights could be learned in an end-to-end manner. To be specific, inputs of the Transformer encoder mainly consist of an Vision Transformer branch and a label embedding branch, which generates suitable image features and label descriptions, respectively. Given an input set of visual features and text labels, the developed Transformer encoder could be accordingly optimized in the training stage with compressed multi-label output layer. In order to obtain sufficient outputs to accurately find images containing similar semantics with the query from the database, we adjust the network by removing the last fully connected layer in the retrieval stage. Specially, images and labels are used for the training stage in a randomly masked manner to enhance the model performance, and no labels are visible in the content-based image retrieval stage. Comprehensive experiments are performed on three multi-label datasets including MS-COCO, NUS-WIDE and VOC2007, demonstrating promising results of our proposed method against the state-of-the-arts for multi-label image retrieval.