Expert Systems with Applications最新文献

{"title":"Research on the generation and evaluation of bridge defect datasets for underwater environments utilizing CycleGAN networks","authors":"Fei Zhang ,&nbsp;Yeyang Gu ,&nbsp;Ling Yin ,&nbsp;Jialei Song ,&nbsp;Chaochao Qiu ,&nbsp;Zhengwei Ye ,&nbsp;Xiangyin Chen ,&nbsp;Jing Wu","doi":"10.1016/j.eswa.2024.125576","DOIUrl":"10.1016/j.eswa.2024.125576","url":null,"abstract":"<div><div>The surface cracks on the underwater structures critically damages the overall reliability of the structures and reduces their strength. It is significant to monitor these cracks in timely manner. Recently, deep learning algorithms have been used for large scale data study and predictions. However, deep supervised learning algorithms need to get training on large scale data set which is time consuming and difficult to apply on the underwater structures. Therefore, it is highly needed to address these issues. Current research proposes an improved cycle-constraint generative adversarial algorithm for the timely detection of surface cracks in underwater structures. It utilizes an enhanced cycle-consistent generative adversarial network (CycleGAN). The proposed algorithm uses image processing techniques including DeblurGAN and Dark channel prior methods to get quality of dataset from underwater structures. The proposed Algorithm introduces a novel cross-domain VGG-cosine similarity assessment to precisely evaluate the performance of proposed algorithm to retain crack information etc. Moreover, performance of proposed algorithm is evaluated through both qualitative and quantitative methods. The quantitative results are directly obtained from the visual results are presented which are generated by the proposed Algorithm. Whereas, the performance of proposed algorithm based on quantitative results is obtained from metrics including PSNR, SSIM, and FID. Experimental results indicates that the proposed algorithm outperforms the original CycleGAN. End results indicate that the proposed algorithm decreased the value of FID by 20 % and increased the values of PSNR and SSIM by 2.37 % and 3.33 % respectively. Quantitative and qualitative results of the proposed algorithm give significant advantages during creating of surface crack images.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125576"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A framework to Prediction occupant injuries in rotated seating arrangements","authors":"Alexander Diederich,&nbsp;Christophe Bastien,&nbsp;Michael Blundell","doi":"10.1016/j.eswa.2024.125698","DOIUrl":"10.1016/j.eswa.2024.125698","url":null,"abstract":"<div><div>This paper describes an innovative computational framework that can address the passive safety of occupants and the prediction of injuries for a wide range of rotated seat arrangements in future autonomous vehicles. An Active Human Model (AHM) wearing a 3-point seat belt with kinematics previously validated using test data was positioned in the rotated seats and simulations were performed for a pre-crash braking phase followed by a frontal collision. The pre-crash braking pulse was parameterised for intensity and shape, both of which affect the collision speed. Computer routines were created to adjust the restraint system response to the impact speed, as well as the crash pulse pattern, based on a Honda Accord MY2011 Finite Element (FE) model. A Design of Experiments (DoE) study was created to explore 400 possible scenarios, and 12 standard injury responses were extracted and converted into AIS2 + and AIS3 + risk values. A Reduced Order Model (ROM), based on the Proper Orthogonal Decomposition (POD) technique, was trained using the 400 scenarios and used to find the seating positions that resulted in AIS2 + and AIS3 + values higher than the base values obtained with the Honda Accord MY2011 FE model. The paper concludes that this new framework is capable of carry out fast computations of dangerous seating positions and the occupant’s kinematics in seconds. The novel framework provides vehicle designers and vehicle safety teams with the capability to identify potentially dangerous positions for the rotated seating arrangements that are envisaged to feature in the cabins of future autonomous vehicles.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125698"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent font generation system based on stroke inference, mitigating production labor and enhancing design experience","authors":"Bolin Wang ,&nbsp;Kejun Zhang ,&nbsp;Zewen Chen ,&nbsp;Lyukesheng Shen ,&nbsp;Xinyi Shen ,&nbsp;Yu Liu ,&nbsp;Jiang Bian ,&nbsp;Hanshu Shen","doi":"10.1016/j.eswa.2024.125657","DOIUrl":"10.1016/j.eswa.2024.125657","url":null,"abstract":"<div><div>Traditionally, font design has relied on manual craftsmanship by designers, a time-consuming and labor-intensive process that can take over a year to complete a new font family. Despite advancements in computer vision and graphics enabling the automation of font generation, creating high-quality fonts meeting commercial standards remains a technical challenge. Current automatic font generation technologies have not fully met production demands, mainly due to their lack of focus on generating glyphs that can be decomposed into strokes and their ineffective post-processing interaction, particularly for Chinese fonts. This study presents an innovative system for intelligently generating Chinese character fonts. The system utilizes a stroke database created by professional designers and combines font images generated through style transfer learning to perform stroke inference for font generation. The system’s core lies in its unique stroke inference mechanism, accurately identifying and matching strokes within font images to efficiently align with standard stroke data in the database. This approach not only improves the precision of font generation but also streamlines subsequent processing steps. Compared to traditional font design systems, our system shows significant advantages in generating fonts suitable for commercial use. It not only aids designers in enhancing work efficiency but also has the potential to greatly increase the production efficiency of font libraries. Moreover, the system’s design is scalable, offering extensive application prospects for future expansion to other East Asian scripts like Japanese and Korean.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125657"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new parallel cooperative landscape smoothing algorithm and its applications on TSP and UBQP","authors":"Wei Wang ,&nbsp;Jialong Shi ,&nbsp;Jianyong Sun ,&nbsp;Arnaud Liefooghe ,&nbsp;Qingfu Zhang","doi":"10.1016/j.eswa.2024.125611","DOIUrl":"10.1016/j.eswa.2024.125611","url":null,"abstract":"<div><div>Combinatorial optimization problem (COP) is difficult to solve because of the massive number of local optimal solutions in his solution space. Various methods have been put forward to smooth the solution space of COPs, including homotopic convex (HC) transformation for the traveling salesman problem (TSP). This paper extends the HC transformation approach to unconstrained binary quadratic programming (UBQP) by proposing a method to construct a unimodal toy UBQP of any size. We theoretically prove the unimodality of the constructed toy UBQP. After that, we apply this unimodal toy UBQP to smooth the original UBQP by using the HC transformation framework and empirically verify the smoothing effects. Subsequently, we introduce an iterative algorithmic framework incorporating HC transformation, referred as landscape smoothing iterated local search (LSILS). Our experimental analyses, conducted on various UBQP instances show the effectiveness of LSILS. Furthermore, this paper proposes a parallel cooperative variant of LSILS, denoted as PC-LSILS and apply it to both the UBQP and the TSP. Our experimental findings highlight that PC-LSILS improves the smoothing performance of the HC transformation, and further improves the overall performance of the algorithm.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125611"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MuSe-CarASTE: A comprehensive dataset for aspect sentiment triplet extraction in automotive review videos","authors":"Atiya Usmani ,&nbsp;Saeed Hamood Alsamhi ,&nbsp;Muhammad Jaleed Khan ,&nbsp;John Breslin ,&nbsp;Edward Curry","doi":"10.1016/j.eswa.2024.125695","DOIUrl":"10.1016/j.eswa.2024.125695","url":null,"abstract":"<div><div>In the Aspect-Based Sentiment Analysis (ABSA) domain, the Aspect Sentiment Triplet Extraction (ASTE) task has emerged as a pivotal endeavor, offering insights into nuanced aspects, opinions, and sentiment relationships. This paper introduces “MuSe-CarASTE”, an extensive and meticulously curated dataset purpose-built to propel ASTE advancements within the automotive domain. The core emphasis of MuSe-CarASTE is on aspect, opinion, and sentiment triplets, facilitating a comprehensive analysis of product reviews. Comprising transcripts from MuSe-Car’s automotive video reviews, MuSe-CarASTE presents a sub-stantial collection of nearly 28,295 sentences organized into 5,500 segments. Each segment is meticulously annotated with multiple aspects, opinions, and sentiment labels, offering unprecedented granularity for ASTE tasks. The percentage agreement between annotated triples by different annotators over the randomly sampled subset of the dataset is 79.74 %, at similarity threshold <em>τ</em> = 0.60. We also experimented with four baseline models on our datset and report results. The distinctiveness of the dataset emerges from its extension into the automotive domain, shedding light on sentiment dynamics specific to vehicles. With the fusion of extensive content and real-world applicability, MuSe-CarASTE presents a fertile ground for Natural Language Processing (NLP) innovation. Researchers, practitioners, and data scientists can harness MuSe-CarASTE to build and evaluate NLP models tailored for challenges in ASTE. These challenges encompass intricate aspect-opinion relationships, multi-word aspect and opinion extraction, and the subtleties of vague language. Moreover, including aspects not verbatim in sentences introduces a practical dimension to our dataset, enabling real-world applications like review pattern analysis, summarization, and recommender system enhancement. As a pioneering benchmark for NLP model evaluation in ABSA, MuSe-CarASTE integrates content richness, real-world context, and sentiment complexity. The integration empowers the development of accurate, adaptable, and insightful sentiment analysis models within the automotive review landscape.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125695"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTIFTrack: Continuous Temporal Information Fusion for object track","authors":"Zhiguo Zhang ,&nbsp;Zhiqing Guo ,&nbsp;Liejun Wang,&nbsp;Yongming Li","doi":"10.1016/j.eswa.2024.125654","DOIUrl":"10.1016/j.eswa.2024.125654","url":null,"abstract":"<div><div>In visual tracking tasks, researchers usually focus on increasing the complexity of the model or only discretely focusing on the changes in the object itself to achieve accurate recognition and tracking of the moving object. However, they often overlook the significant contribution of video-level linear temporal information fusion and continuous spatiotemporal mapping to tracking tasks. This oversight may lead to poor tracking performance or insufficient real-time ability of the model in complex scenes. Therefore, this paper proposes a real-time tracker, namely Continuous Temporal Information Fusion Tracker (CTIFTrack). The key of CTIFTrack lies in its well-designed Temporal Information Fusion (TIF) module, which cleverly performs a linear fusion of the temporal information between the <span><math><mrow><mrow><mo>(</mo><mi>t</mi><mtext>-</mtext><mn>1</mn><mo>)</mo></mrow><mtext>-th</mtext></mrow></math></span> and the <span><math><mrow><mi>t</mi><mtext>-th</mtext></mrow></math></span> frames and completes the spatiotemporal mapping. This enables the tracker to better understand the overall spatiotemporal information and contextual spatiotemporal correlations within the video, thereby having a positive impact on the tracking task. In addition, this paper also proposes the Object Template Feature Refinement (OTFR) module, which effectively captures the global information and local details of the object, and further improves the tracker’s understanding of the object features. Extensive experiments are conducted on seven benchmarks, such as LaSOT, GOT-10K, UAV123, NFS, TrackingNet, VOT2018 and OTB-100. The experimental results validate the significant contribution of the TIF module and OTFR module to the tracking task, as well as the effectiveness of CTIFTrack. It is worth noting that while maintaining excellent tracking performance, CTIFTrack also shows outstanding real-time tracking speed. On the Nvidia Tesla T4-16GB GPU, the <span><math><mrow><mi>F</mi><mi>P</mi><mi>S</mi></mrow></math></span> of CTIFTrack reaches 71.98. The code and demo materials will be available at <span><span>https://github.com/vpsg-research/CTIFTrack</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125654"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection of cyberbullying behaviour on social media using Stacked Bi-Gru attention with BERT model","authors":"Mohan K. Mali,&nbsp;Ranjeet R. Pawar,&nbsp;Sandeep A. Shinde,&nbsp;Satish D. Kale,&nbsp;Sameer V. Mulik,&nbsp;Asmita A. Jagtap,&nbsp;Pratibha A. Tambewagh,&nbsp;Punam U. Rajput","doi":"10.1016/j.eswa.2024.125641","DOIUrl":"10.1016/j.eswa.2024.125641","url":null,"abstract":"<div><div>Cyberbullying behaviour has drawn more attention as social media usage has grown. Teen suicide has been related to cyberbullying, among other serious and harmful effects on a person’s life. Using the appropriate natural language processing and machine learning techniques, it is possible to proactively identify bullying content to reduce and eventually eradicate cyberbullying. Accordingly, the article proposed an automated deep-learning model for detecting aggressive activity in cyberbullying. Initially, the data was extracted from the social media platform using Formspring, Instagram and MySpace datasets for perceiving cyberbullying behaviour, then the collected data are input for preprocessing. To remove the raw data, several preprocessing processes have been introduced. They consist of removing stop words, white spaces for punctuation, and changing the comments to lowercase. Lexical Density (LD) has been one of the metrics used to gauge language complexity generally. As a result, the study made use of the Feature Density (FD) to calculate how complicated certain natural language datasets are using the linguistically backed preprocessing model. After preprocessing, the data are input to the feature selection process which selects the pertinent features or attributes to include in predictive modelling and which to leave out. Since, the article proposed a Binary Chimp Optimization (BCO)-based Feature Selection (BCO-FSS) technique, which selects the subset of features for classification performance improvement. The selected features are exploited for cyberbullying behaviour detection. To identify the exploit of social media for cyberbullying text content, the article suggested Stacked Bidirectional Gated Recurrent Unit (SBiGRU) Attention for learning spatial location information and sequential semantic representations using a Bi-GRU. Additionally, the BERT model is employed as a base classifier to recognize and categorise aggressive behaviour in the textual content. The Matlab software is employed for simulation. For accuracy, precision, recall, and F1-Score, this experiment yielded a practically perfect outcome with values of 99.12%, 94.73%, 97.45%, and 93.91% respectively.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125641"},"PeriodicalIF":7.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using cross-domain knowledge augmentation to explore comorbidity in electronic health records data","authors":"Kaiyuan Zhang ,&nbsp;Buyue Qian ,&nbsp;Xiyuan Zhang ,&nbsp;Qinghua Zheng","doi":"10.1016/j.eswa.2024.125644","DOIUrl":"10.1016/j.eswa.2024.125644","url":null,"abstract":"<div><div>Research concentrating on specific diseases or employing single datasets, such as medical histories and thematic data, has garnered considerable attention. However, there has been limited investigation into comorbidities. Although some ad-hoc methods have been utilized in the medical field, there is a scarcity of systematic approaches to address this challenge. The task of expressing patient features using heterogeneous and cross-domain data presents considerable difficulties. Directly mapping this data into a matrix frequently results in issues such as high dimensionality, sparsity, redundancy, and noise. Additionally, given the critical role of supervisory information in medicine, acquiring accurate information is paramount. To address these issues, we propose an enhanced clustering method that capitalizes on cross-domain knowledge augmentation. This method can iteratively learn clustering outcomes and cross-domain knowledge. The cross-domain knowledge matrix produced by our approach can be interpreted as a measure of similarity between instances across domains. We validate our proposed model in real-world electronic health records (EHR) data, and achieve significant performance improvement compared to the baseline method, successfully completing the task of exploring comorbidity. Due to the privacy of EHR data, we also conduct extensive experiments on the publicly available datasets DBLP and UCI. The experimental results show that our algorithm is superior to the baseline algorithm and has strong generality.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125644"},"PeriodicalIF":7.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing rumor detection with data augmentation and generative pre-trained transformer","authors":"Mojgan Askarizade","doi":"10.1016/j.eswa.2024.125649","DOIUrl":"10.1016/j.eswa.2024.125649","url":null,"abstract":"<div><div>The advent of social networks has facilitated the rapid dissemination of false information, including rumors, leading to significant societal and individual damages. Extensive research has been dedicated to rumor detection, ranging from machine learning techniques to neural networks. However, the existing methods could not learn the deep concepts of the rumor text to detect the rumor. In addition, imbalanced datasets in the rumor domain reduce the effectiveness of these algorithms. This study addresses this challenge by leveraging the Generative Pre-trained Transformer 2 (GPT-2) model to generate rumor-like texts, thus creating a balanced dataset. Subsequently, a novel approach for classifying rumor texts is proposed by modifying the GPT-2 model. We compare our results with state-of-art machine learning and deep learning methods as well as pre-trained models on the PHEME, Twitter15, and Twitter16 datasets. Our findings demonstrate that the proposed model, implementing advanced artificial intelligence techniques, has improved accuracy and F-measure in the application of detecting rumors compared to previous methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125649"},"PeriodicalIF":7.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing inertial sensor-based sports activity recognition through reduction of the signals and deep learning","authors":"Pajak Grzegorz ,&nbsp;Patalas-Maliszewska Justyna ,&nbsp;Krutz Pascal ,&nbsp;Rehm Matthias ,&nbsp;Pajak Iwona ,&nbsp;Schlegel Holger ,&nbsp;Dix Martin","doi":"10.1016/j.eswa.2024.125693","DOIUrl":"10.1016/j.eswa.2024.125693","url":null,"abstract":"<div><div>The increasing demand for sport training and health monitoring aligns with contemporary lifestyle trends. Developing a system to support sport training and verify exercise correctness can significantly enhance the acquisition of detailed, subject-specific data. This study aims to evaluate the accuracy of sport exercise recognition while minimizing the number of sensors required. The dataset includes 8,968 samples of exercises such as bar dips, squats, dips, lunges, pull-ups, sit-ups, and push-ups. Data were collected from 60 signals using mobile sensors positioned at the chest, right hand, and right foot of 21 subjects. The objective is to identify the most efficient method for recognizing these activities. Our methodology involved experiments with 21 participants in a custom-built setup and the application of deep learning techniques. Initially, a novel algorithm identified the optimal set of signals. We then tested two scenarios: training a Convolutional Neural Network (CNN) on raw signals and on pre-processed signals to reduce noise. The findings indicate that the magnetic field (MF) signal is crucial for recognizing exercises in both filtered and unfiltered data sets. The CNN’s accuracy was 2–3% higher with unfiltered data and remained robust at 93.7% for training and 90.0% for testing, despite a reduction in model complexity. This method’s practical implications are significant, enhancing sports training systems by reducing the number of sensors needed, thereby improving user comfort. Additionally, it contributes valuable insights to the field of Human Activity Recognition.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125693"},"PeriodicalIF":7.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}