Tang poetry semantic correlation computing is critical in many applications, such as searching, clustering, automatic generation of poetry and so on. Aiming to increase computing efficiency and accuracy of semantic relatedness, we improved the process of latent semantic analysis (LSA). In this paper, we adopted "representation of words semantic" instead of "words-by-poems" to The ability to quantify semantic relatedness of words in poems should be an integral part of semantic analysis, and underlies many fundamental tasks in NLP, including information retrieval, word sense disambiguation, and text clustering, etc. In contrast to semantic similarity, which is the special case of relatedness, the notion of relatedness is more general than that of similarity like Budanitsky et al [7] argued, as the latter subsumes many different kind of specific relations, including metonymy, antonym, functional association, and others. In this paper we deal with semantic relatedness.Semantic relatedness computing of natural language texts requires encoding vast amount of world knowledge. Until recently, prior work of linguistic resources using pursued two main directions. One is lexical databases such as WordNet [8], Wikipedia [9], encodes relations between words such as synonymy, hypernymy, and the other is large-scale text corpora, provide statistical corpus for computer learning like Latent Semantic Analysis (LSA) [10].But in general computing of modern language semantic relatedness, the least resources used are knowledge-free approaches that rely exclusively on the corpus data themselves. Under the corpus-based approach, word relationships are often derived from their co-occurrence distribution in a corpus [11]. With the introduction of machine readable dictionaries, lexicons, thesauri, and taxonomies, these manually built pseudo-knowledge bases provide a natural framework for organizing words or concepts into a semantic space. Kozima and Furugori [12] measured word distance by adaptive scaling of a vector space generated from LDOCE (Longman Dictionary of Contemporary English). Morris and Hirst [13] used Roget's thesaurus to detect word semantic relationships. With the recently developed lexical taxonomy WordNet [14], many researches have taken the advantage of this broad-coverage taxonomy to study word/concept relationships [15].
Protozoa, such as Ceratium and Paramecium, play a fundamental role in establishing sustainable ecosystems. The distribution and classification of certain protozoa and their species are informative indicators to evaluate environmental quality. However, protozoa analysis is traditionally performed by molecular biological (DNA, RNA) or morphological methods, which are time-consuming and require an experienced laboratory operator. In this work, we adopt a deep learning-based network to solve the protozoa classification task. This method utilizes microscope images to help researchers analyse the protozoa population and species, reducing the cost of experimental sample storage and relieving the burden on laboratory operators. However, the shape and size of protozoa vary greatly, which places a great burden on the optimization of DCNN feature distillation. It is a great challenge to build a fast and precise protozoa analysis image. We present a new version of YOLO-v5 with better performance and extend it with instance segmentation called PR-YOLO. Building on the original YOLOv5, we added two extra parallel branches to PR-YOLO, which perform different segmentation subtasks: (1) a branch generates a set of prototype masks (images); (2) the other branch predicts a set of mask coefficients corresponding to prototype masks for each instance mask generation. Then, to improve the classification accuracy, we introduced transformer encoding blocks and lightweight Convolution Block Attention Modules (CBAMs) to explore the prediction potential with a self-attention mechanism. To quantitatively evaluate the performance of PR-YOLO, a comprehensive experiment was carried out on the hand-segmented microscopic protozoa images. Our model obtained the best results, with average classification accuracy of 96.83% and mean Average Precision(mAP) of 86.92% with a speed of 25.2 fps, which proves that the method has high robustness in this application field.
With the emergence and development of virtual reality technology, it gradually penetrates various fields of society and explores a new model for experimental teaching. Based on the construction concept of virtual simulation teaching platform, this paper combines the course of rice wine brewing technology, mainly elaborates on the application and current status of domestic virtual simulation teaching platform, the construction, and design of virtual simulation teaching platform of rice wine brewing course, and the implementation method of virtual simulation teaching platform system, thereby promote the application of simulation teaching in such technology courses and explore the reform of the teaching model of the rice wine brewing course to improve the quality of teaching.
Hydrogen energy vehicles are being increasingly widely used. To ensure the safety of hydrogenation stations, research into the detection of hydrogen leaks is required. Offline analysis using data machine learning is achieved using Spark SQL and Spark MLlib technology. In this study, to determine the safety status of a hydrogen refueling station, we used multiple algorithm models to perform calculation and analysis: a multi-source data association prediction algorithm, a random gradient descent algorithm, a deep neural network optimization algorithm, and other algorithm models. We successfully analyzed the data, including the potential relationships, internal relationships, and operation laws between the data, to detect the safety statuses of hydrogen refueling stations.
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