Semantic Features Research Articles

A Corpus-based Behavioral Profile Study of Synonymous Expressions Meaning “to Start”

Synonymy is an extremely significant component in language learning, and the usage of synonymy is rather flexible and difficult to distinguish. In this research, the author employs a corpus-based behavioral profile approach to study the usage of a set of English synonymous expressions meaning “to start”, namely, start, begin, commence, launch, set out. The author first searches in American Contemporary English Corpus to explore the overall usage frequency and specific genre of each synonymous expression, then randomly selects 500 sentences, and makes manual annotation with 12 ID tag and 46 ID tag levels. Later, hierarchical clustering analysis and correspondence analysis have been employed to examine the semantic differences and usage structures of this set of expressions.The results shows that: ① The overall usage frequency varies greatly—from the highest to the lowest are start, begin, launch, set out, commence. ② They are used in various genres. Start is mainly used in informal styles including blogs, while the other four are mainly used in formal styles, including fictions and magazines. ③ Begin and set out have the greatest semantic similarity; start, begin, and set out form one semantic branch, and commence and launch form another, and there is a huge difference between these two branches. ④ There are great differences between the five expressions and their morphological, syntactic and semantic changes.Quantitative analysis method has been used to reveal the different semantic features and usage of a set of synonymous expressions, which is conducive to both promote the study of synonymous relations, and verify the effectiveness and applicability of the behavioral profile approach in semantic study.

Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness

The accurate segmentation of skin lesions plays an important role in the diagnosis and treatment of skin cancers. However, skin lesion areas are rich in details and local features, including the appearance, size, shape, texture, etc., which pose challenges for the accurate localization and segmentation of the target area. Unfortunately, the consecutive pooling and stride convolutional operations in existing convolutional neural network (CNN)-based solutions lead to the loss of some spatial information and thus constrain the accuracy of lesion region segmentation. In addition, using only the traditional loss function in CNN cannot ensure that the model is adequately trained. In this study, a generative adversarial network is proposed, with global and local semantic feature awareness (GLSFA-GAN) for skin lesion segmentation based on adversarial training. Specifically, in the generator, a multi-scale localized feature fusion module and an effective channel-attention module are designed to acquire the multi-scale local detailed information of the skin lesion area. In addition, a global context extraction module in the bottleneck between the encoder and decoder of the generator is used to capture more global semantic features and spatial information about the lesion. After that, we use an adversarial training strategy to make the discriminator discern the generated labels and the segmentation prediction maps, which assists the generator in yielding more accurate segmentation maps. Our proposed model was trained and validated on three public skin lesion challenge datasets involving the ISIC2017, ISIC2018, and HAM10000, and the experimental results confirm that our proposed method provides a superior segmentation performance and outperforms several comparative methods.

Lightweight monocular depth estimation using a fusion-improved transformer

The existing deep estimation networks often overlook the issue of computational efficiency while pursuing high accuracy. This paper proposes a lightweight self-supervised network that combines convolutional neural networks (CNN) and Transformers as the feature extraction and encoding layers for images, enabling the network to capture both local geometric and global semantic features for depth estimation. First, depth-separable convolution is used to construct a dilated convolution residual module based on a shallow network to improve the shallow CNN feature extraction receptive field. In the transformer, a multidepth separable convolution head transposed attention module is proposed to reduce the computational burden of spatial self-attention. In the feedforward network, a two-step gating mechanism is proposed to improve the nonlinear representation ability of the feedforward network. Finally, the CNN and transformer are integrated to implement a depth estimation network with a local-global context interaction function. Compared with other lightweight models, this model has fewer model parameters and higher estimation accuracy. It also has better generalizability for different outdoor datasets. Additionally, the inference speed can reach 87 FPS, achieving better real-time performance and accounting for both inference speed and estimation accuracy.

Spatio-temporal fusion with motion masks for the moving small target detection from remote-sensing videos

Moving small target detection is an eye-catching research topic, full of great challenges in object detection field. Its primary difficulties exist in (i) the very small size proportion of the target region in background images, (ii) the quite weak contrast to background and (iii) the perception of slight target motion. Currently-existing detection methods mainly utilize the image features from spatial domain. This kind of features is quite weak to small targets. More new feature kinds need to be concerned. In view of these, besides traditional image clues, motion features are currently attracting more and more attention in small target detection. In order to promote detection performance, this paper proposes a Spatio-Temporal Fusion Network (STFNet) with two parallel feature extraction branches for detecting moving small targets. In this network, one branch is designed to capture the traditional semantic features from spatial domain, and the other is devised to perceive the slight target motion features hidden in temporal domain. Meanwhile, to optimize the extraction of spatio-temporal features, a group of motion masks is specially designed to guide feature extractors to pay more precise attention to the positions where small targets appear. Moreover, we design a new bridging module to enhance the cross-domain and cross-scale fusion of spatio-temporal features. Through extensive comparison and ablation experiments on seven sub-datasets, it is proved that our new STFNet is effective and it is obviously superior to the compared ones in detecting moving small targets from satellite sequence images. It achieves a Precision of 0.93, an mAP50 of 71.10% and an F1 of 0.84, evidently higher than existing state-of-the-art methods. Our codes are available at https://github.com/UESTC-nnLab/STF.

AerialIRGAN: unpaired aerial visible-to-infrared image translation with dual-encoder structure

Due to the high cost of equipment and the constraints of shooting conditions, obtaining aerial infrared images of specific targets is very challenging. Most methods using Generative Adversarial Networks for translating visible images to infrared greatly depend on registered data and struggle to handle the diversity and complexity of scenes in aerial infrared targets. This paper proposes a one side end-to-end unpaired aerial visible-to-infrared image translation algorithm, termed AerialIRGAN. AerialIRGAN introduces a dual-encoder structure, where one encoder is designed based on the Segment Anything Model to extract deep semantic features from visible images, and the other encoder is designed based on UniRepLKNet to capture small-scale patterns and sparse patterns from visible images. Subsequently, AerialIRGAN constructs a bridging module to deeply integrate the features of both encoders and their corresponding decoders. Finally, AerialIRGAN proposes a structural appearance consistency loss to guide the synthetic infrared images to maintain the structure of the source image while possessing distinct infrared characteristics. The experimental results show that compared to the existing typical infrared image generation algorithms, the proposed method can generate higher-quality infrared images and achieve better performance in both subjective visual description and objective metric evaluation.

MULTICOMPONENCE AS A STRUCTURAL PECULIARITY OF MILITARY TERMS IN FOREIGN MASS MEDIA INTO RUSSIAN TRANSLATION

This article delves into the important analysis of the structural peculiarities of multicomponent military terms, a topic of increasing importance due to the need to describe professional objects and concepts, identify complex ideas, and unearth new dimensions of the studied phenomena. The prevalence of multicomponent terminology in foreign mass media resources underscores its significance in the media landscape. The purpose of the article is to explore the structural characteristics of multicomponent military terms extracted from mass media sources, providing a Russian translation. We also seek to identify the primary formulas and constituent components of these terms. By doing so, we hope to contribute to a better understanding of how English military terms can be formed and the development of a terminological dictionary that will include the latest examples of multicomponent military terms. The scientific novelty of the research lies in its focus on a particular and specialized domain of terminological studies, which will help to comprehend the challenges associated with understanding the structural and semantic features of multicomponent military terms, their word formation and constituent components frequently used in their structural models. The results obtained can also serve as a foundation for other fields of terminology.The research methodology is a combination of different research methods, such as the method of continuous sampling, component analysis, and word-formation analysis, which are used to analyze the selected material, and the quantitative approach helps to deal with the numerical data. The systematized examples of multicomponent military terms, categorized into two-, three-, and four-component terms and compound terms with hyphenated spelling, are accompanied by the corresponding translation. The findings of this research will equip translators, researchers, and practitioners with a deeper understanding of the challenges and strategies involved in analyzing the structural models of multicomponent military terms, thereby enhancing their professional capabilities.

3D Point Cloud Semantic Segmentation based on Multi-scale Dense Nested Networks

Aiming at the problem that the relationship between geometric features and semantic features is ignored in the point cloud data downsampling process, which leads to inaccurate segmentation of object boundaries and structural details, this paper proposes a 3D point cloud semantic segmentation network based on multi-scale dense nested type. Firstly, a dense nested network architecture is constructed by nesting multiple multi-scale feature fusion modules to fuse multi-scale features of different directions between encoder-decoder paths, so as to effectively propagate local ge-ometric context information and enhance the ability of cross-scale information interaction. Secondly, a local feature aggregation unit is constructed in the multi-scale feature fusion module, which strengthens the structural awareness within the local point set based on graph convolution and attention mechanism, and promotes the complementarity of local geometric features and abstract semantic information. Then, the cross-layer multi-loss supervision module is combined to further optimize the multi-scale feature propagation, which makes the network training more stable and improves the point cloud segmentation accuracy. Finally, this paper verified the proposed network on the S3DIS dataset. The experimental results show that the proposed network has the mean intersection over Union of 71.2% and the overall accuracy of 88.7%, which is 1.2 and 0.7 percentage points higher than RandLA-Net, respectively, which proves that the proposed network can effectively improve the accuracy of 3D point cloud semantic segmentation.

TDAD: Self-supervised industrial anomaly detection with a two-stage diffusion model

Visual anomaly detection has emerged as a highly applicable solution in practical industrial manufacturing, owing to its notable effectiveness and efficiency. However, it also presents several challenges and uncertainties. To address the complexity of anomaly types and the high cost associated with data annotation, this paper introduces a self-supervised learning framework called TDAD, based on a two-stage diffusion model. TDAD consists of three key components: anomaly synthesis, image reconstruction, and defect segmentation. It is trained end-to-end, with the goal of improving pixel-level segmentation accuracy of anomalies and reducing false detection rates. By synthesizing anomalies from normal samples, designing a diffusion model-based reconstruction network, and incorporating a multiscale semantic feature fusion module for defect segmentation, TDAD achieves state-of-the-art performance in image-level detection and anomaly localization on challenging and widely used datasets such as MVTec and VisA benchmarks.

Optimizing feature extraction and fusion for high-resolution defect detection in solar cells

In this paper, we propose a novel architecture for defect detection in electroluminescent images of polycrystalline silicon solar cells, addressing the challenges posed by subtle and dispersed defects. Our model, based on a modified Swin Transformer, incorporates key innovations that enhance feature extraction and fusion. We replace the conventional self-attention mechanism with a novel group self-attention mechanism, increasing the mAP50:5:95 score from 50.12 % to 52.98 % while reducing inference time from 74 ms to 62 ms. We also introduce a spatial displacement with shift convolution module, replacing the traditional Multi-Layer Perceptron, which further enhances the model's receptive field and improves precision and recall. Additionally, our fast multi-scale feature fusion mechanism effectively combines high-resolution details with high-level semantic features from different network layers, optimizing defect detection accuracy. Experimental results on the PVEL-AD dataset demonstrate that our model achieves the highest mAP50 score of 83.11 % and an F1-Score of 84.33 %, surpassing state-of-the-art models while maintaining a competitive inference time of 66.3 ms. These findings highlight the effectiveness of our innovations in improving defect detection accuracy and computational efficiency, making our model a robust solution for quality assurance in solar cell manufacturing.

The Neurophysiological Costs of Learning in a Noisy Classroom: An Ecological Virtual Reality Study.

Many real-life situations can be extremely noisy, which makes it difficult to understand what people say. Here, we introduce a novel audiovisual virtual reality experimental platform to study the behavioral and neurophysiological consequences of background noise on processing continuous speech in highly realistic environments. We focus on a context where the ability to understand speech is particularly important: the classroom. Participants (n = 32) experienced sitting in a virtual reality classroom and were told to pay attention to a virtual teacher giving a lecture. Trials were either quiet or contained background construction noise, emitted from outside the classroom window. Two realistic types of noise were used: continuous drilling and intermittent air hammers. Alongside behavioral outcomes, we measured several neurophysiological metrics, including neural activity (EEG), eye-gaze and skin conductance (galvanic skin response). Our results confirm the detrimental effect of background noise. Construction noise, and particularly intermittent noise, was associated with reduced behavioral performance, reduced neural tracking of the teacher's speech and an increase in skin conductance, although it did not have a significant effect on alpha-band oscillations or eye-gaze patterns. These results demonstrate the neurophysiological costs of learning in noisy environments and emphasize the role of temporal dynamics in speech-in-noise perception. The finding that intermittent noise was more disruptive than continuous noise supports a "habituation" rather than "glimpsing" hypothesis of speech-in-noise processing. These results also underscore the importance of increasing the ecologically relevance of neuroscientific research and considering acoustic, temporal, and semantic features of realistic stimuli as well as the cognitive demands of real-life environments.

A Highly Accurate Adverse Drug Reactions (ADR) Detection from Medical Forum Comments Using Long Short-Term Memory Networks

Purpose: Adverse Drug Reactions (ADR) classification is useful in modern medical diagnostics and related applications. ADR is an example of how medical information is frequently accessible on social media platforms for healthcare, where people can share their experiences with treatments on desktop computers and mobile devices. Many researchers are interested in gathering valuable medical data from social media for the ADR system training and classification process. Materials and Methods: This research explores the effects of three aspects on recognizing ADR mentions in social media for the medical field and proposes a deep neural network of Long Short-Term Memory (LSTM) neural networks to do so. The comments are collected from various social media platforms to implement the ADR system with proper training and testing processes. The texts from the dataset are initially preprocessed by using a data filtering and clustering process to remove the input data's redundant information to increase the training process's quality. Characteristic features, such as semantic features and text statistics, are extracted from the input text using the American Standard Code for Information Interchange (ASCII) array. Further, the features are converted and fed to LSTM networks for training and validation. Results and Conclusion: This work is evaluated using two datasets, CODEC, and ADR Corpus datasets are used to evaluate the performance of the proposed ADR technique via multiple angles. Via extensive experiments, this work achieved 99.79 accuracy, 98.37 sensitivity, 97.63 specificity, 99.72 precision, 98.39 recall, 97.62 F1-score for the CODEC dataset, 98.16 for accuracy, 99.19 for sensitivity, 98.49 for specificity, 99.49 for precision, 96.72 for recall, and 93.16 for F1-score for ADR corpus, respectively.

Imaging of Osteosarcoma: Presenting Findings, Metastatic Patterns, and Features Related to Prognosis

Background: Osteosarcomas are rare malignancies (<1% of all cancers) that produce an osteoid matrix. Osteosarcomas are the second most frequent type of primary bone tumor after multiple myeloma and the most prevalent primary bone tumor in children. The spectrum of imaging findings of these malignancies varies significantly, reflecting different histological subtypes. For instance, conventional osteosarcoma typically presents with a mixed radiological pattern (lytic and bone mineralization) or with a completely eburneous one; aggressive periosteal reactions such as sunburst, Codman triangle, and soft-tissue components are frequently displayed. On the other hand, telangiectatic osteosarcoma usually presents as a purely lytic lesion with multiple fluid–fluid levels on MRI fluid-sensitive sequences. Other typical and atypical radiological patterns of presentation in other subtypes of osteosarcomas are described in this review. In addition to the characteristics associated with osteosarcoma subtyping, this review article also focuses on imaging features that have been associated with patient outcomes, namely response to chemotherapy and event-free and overall survivals. This includes simple semantic radiological features (such as tumor dimensions, anatomical location with difficulty of radical surgery, occurrence of pathological fractures, and presence of distant metastases), but also quantitative imaging parameters from diffusion-weighted imaging, dynamic contrast-enhanced MRI, and 18F-FDG positron emission tomography and radiomics approaches. Other particular features are described in the text. Overall, this comprehensive literature review aims to be a practical tool for oncologists, pathologists, surgeons, and radiologists involved in these patients’ care.

Fusing temporal and semantic dependencies for session-based recommendation

Session-based recommendation (SBR) predicts the next item in user sequences. Existing research focuses on item transition patterns, neglecting semantic information dependencies crucial for understanding users’ preferences. Incorporating semantic characteristics is vital for accurate recommendations, especially in applications like user purchase sequences. In this paper, to tackle the above issue, we novelly propose a framework that hierarchically fuses temporal and semantic dependencies. Technically, we present the Item Transition Dependency Module and Semantic Dependency Module based on the whole session set: (i) Item Transition Dependency Module is exclusively to learn the item embeddings through temporal relations and utilizes item transitions from both global and local levels; (ii) Semantic Dependency Module develops mutually independent embeddings of both sessions and items via stable interaction relations. In addition, under the unified organization of the Cross View, semantic information is adaptively incorporated into the temporal dependency learning and used to improve the performance of SBR. Extensive experiments on three large-scale real-world datasets show the superiority of our framework over current state-of-the-art methods. In particular, our model improves its performance over SOTA on all three datasets, with 5.5%, 0.2%, and 3.0% improvements on Recall@20, and 5.8%, 4.6%, and 2.0% improvements on MRR@20, respectively.

Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection

At present, a large number of rumors are mixed in with various kinds of news, such as current affairs, politics, social economy, and military activities, which seriously reduces the credibility of Internet information and hinders the positive development of various fields. In previous research on rumors, most scholars have focused their attention on the textual features, contextual semantic features, or single-emotion features of rumors but have not paid attention to the chain reaction caused by the hidden emotions in comments in social groups. Therefore, this paper comprehensively uses the emotional signals in rumor texts and comments to extract emotional features and determines the relationship between them to establish dual-emotion features. The main research achievements include the following aspects: (1) this study verifies that, in the field of affective characteristics, the combination of rumor-text emotion and comment emotion is superior to other baseline affective characteristics, and the detection performance of each component is outstanding; (2) the results prove that the combination of dual-emotion features and a semantic-feature-based detector (BiGRU and CNN) can improve the effectiveness of the detector; (3) this paper proposes reconstructing the dataset according to time series to verify the generalization ability of dual affective features; (4) the attention mechanism is used to combine domain features and semantic features to extract more fine-grained features. A large number of data experiments show that the dual-emotion features can be effectively compatible with an existing rumor detector, enhance the detector’s performance, and improve the detection accuracy.

TAWFN: a deep learning framework for protein function prediction.

Proteins play pivotal roles in biological systems, and precise prediction of their functions is indispensable for practical applications. Despite the surge in protein sequence data facilitated by high-throughput techniques, unraveling the exact functionalities of proteins still demands considerable time and resources. Currently, numerous methods rely on protein sequences for prediction, while methods targeting protein structures are scarce, often employing convolutional neural networks (CNN) or graph convolutional networks (GCNs) individually. To address these challenges, our approach starts from protein structures and proposes a method that combines CNN and GCN into a unified framework called the two-model adaptive weight fusion network (TAWFN) for protein function prediction. First, amino acid contact maps and sequences are extracted from the protein structure. Then, the sequence is used to generate one-hot encoded features and deep semantic features. These features, along with the constructed graph, are fed into the adaptive graph convolutional networks (AGCN) module and the multi-layer convolutional neural network (MCNN) module as needed, resulting in preliminary classification outcomes. Finally, the preliminary classification results are inputted into the adaptive weight computation network, where adaptive weights are calculated to fuse the initial predictions from both networks, yielding the final prediction result. To evaluate the effectiveness of our method, experiments were conducted on the PDBset and AFset datasets. For molecular function, biological process, and cellular component tasks, TAWFN achieved area under the precision-recall curve (AUPR) values of 0.718, 0.385, and 0.488 respectively, with corresponding Fmax scores of 0.762, 0.628, and 0.693, and Smin scores of 0.326, 0.483, and 0.454. The experimental results demonstrate that TAWFN exhibits promising performance, outperforming existing methods. The TAWFN source code can be found at: https://github.com/ss0830/TAWFN.

Vulnerability detection based on transformer and high‐quality number embedding

SummarySoftware vulnerability detection is an important problem in software security. In recent years, deep learning offers a novel approach for source code vulnerability detection. Due to the similarities between programming languages and natural languages, many natural language processing techniques have been applied to vulnerability detection tasks. However, specific problems within vulnerability detection tasks, such as buffer overflow, involve numerical reasoning. For these problems, the model needs to not only consider long dependencies and multiple relationships between statements of code but also capture the magnitude property of numerical literals in the program through high‐quality number embeddings. Therefore, we propose VDTransformer, a Transformer‐based method that improves source code embedding by integrating word and number embeddings. Furthermore, we employ Transformer encoders to construct a hierarchical neural network that extracts semantic features from the code and enables line‐level vulnerability detection. To evaluate the effectiveness of the method, we construct a dataset named OverflowGen based on templates for buffer overflow. Experimental comparisons on OverflowGen with a well‐known static vulnerability detector and two state‐of‐the‐art deep learning‐based methods confirm the effectiveness of VDTransformer and the importance of high‐quality number embeddings in vulnerability detection tasks involving numerical features.

Multimodality-guided Visual-Caption Semantic Enhancement

Video captions generated with single modality, e.g. video clips, often suffer from insufficient event discovery and inadequate scene description. Therefore, this paper aims to improve the quality of captions by addressing these issues through the integration of multi-modal information. Specifically, We first construct a multi-modal dataset and introduce the triplet annotations of video, audio and text, fostering a comprehensive exploration about the associations between different modalities. Build upon this, We propose to explore the collaborative perception of audio and visual concepts to mitigate inaccuracies and incompleteness in captions in vision-based benchmarks by incorporating audio-visual perception priors. To achieve this, we extract effective semantic features from visual and auditory modalities, bridge the semantic gap between audio-visual modalities and text, and form a more precise knowledge graph multimodal coherence checking and information pruning mechanism. Exhaustive experiments demonstrate that the proposed approach surpasses existing methods and generalizes well with the assistance of ChatGPT.

From Sound to Meaning: Navigating Wernicke's Area in Language Processing.

Wernicke's area, a critical brain region associated with language comprehension, was first identified by Carl Wernicke in the late 19th century. Situated in the left hemisphere's posterior superior temporal gyrus, this area is essential for processing auditory and visual language inputs. It integrates semantic and syntactic information, playing a key role in meaningful communication. The development of Wernicke's area during infancy and childhood is marked by rapid growth and refinement influenced by early language exposure and environmental stimuli. Neuroplasticity, the brain's ability to reorganize and adapt, is crucial for recovery from language impairments such as Wernicke's aphasia. This capacity for reorganization includes synaptic plasticity and axonal sprouting, which facilitate recovery through targeted rehabilitation and enriched environments. Recent research utilizing advanced neuroimaging and neuroanatomical tracing techniques has elucidated the connectivity of Wernicke's area with other language-related regions, such as Broca's area. Functional studies have revealed its specialized roles in processing different aspects of language, including phonological, semantic, and syntactic features. Moreover, investigations into language disorders and potential therapeutic interventions underscore the importance of harnessing neuroplasticity for effective treatment. Emerging technologies, such as non-invasive brain stimulation and multimodal imaging, offer promising avenues for further exploration of Wernicke's area and its role in language functions. These innovations hold the potential to enhance our understanding of language processing and improve therapeutic strategies for language impairments. In conclusion, Wernicke's area is central to language comprehension, and genetic and environmental factors influence its development. Understanding neuroplasticity and leveraging advanced research technologies can significantly advance our ability to address language-related disorders and enhance patient outcomes.

Hamlet and Inoculation

ABSTRACT This article surveys Shakespeare’s biopolitical thought, with a focus on the semantic feature of “inoculat[ion]” in Hamlet. Today, the word “inoculation” means a way to produce artificial immunity by inserting a pathogenic material taken from an external entity through a small incision. This medical procedure, widely practiced in the eighteenth century, is called inoculation because it was like grafting—mixing one body with another. By using “inoculat[ion]” as a rhetorical device, Hamlet explores the politics of immunity before the dawn of modern immunology. Shakespeare’s work negotiates legal and medical thoughts on immunity: in particular, Hamlet is an intriguing text because it balances two different features of immunity, signifying at once juridical exception (as a signature of sovereign authority) and the negative protection of a body (through the metaphor of corpus politicum). Like the metaphor of inoculation (i.e. grafting), a practice that needs the other’s body, Hamlet shows that the body politic can be secured only when the realm elects the same invading foreign entity. In Shakespeare’s early modern imagination, immunity is not a strict boundary between self and non-self; rather, it is a much more negotiable boundary open to alterity and creaturely existences.

RETRACTED: Design and Implementation of Folk Arts and Crafts Resource Library System Based on Ontology Description and Random Matrix

This article first constructs an ontology, designs the ontology of the resource library, and then uses the ontology to generate formatted SMD. It uses semantic elements to standardize the description of system resources, organizes folk arts and crafts resources in a machine understandable manner, and presents semantic features to users in an intuitive manner. And use random matrix theory to extract the correlation between data from massive amounts of data. The experimental results show that the running time of the system algorithm reaches 1.8 seconds, effectively avoiding the algorithm's extended state estimation falling into local optima and greatly reducing the computational complexity of the algorithm. This study is conducive to promoting the role of resource library systems and improving retrieval efficiency.