Expert Systems with Applications最新文献

{"title":"PAD: Popularity-aware debiasing for high-value item recommendation","authors":"Yuchen Zheng ,&nbsp;Dongming Zhao ,&nbsp;Xiangrui Cai ,&nbsp;Yanlong Wen ,&nbsp;Xiaojie Yuan","doi":"10.1016/j.eswa.2025.129830","DOIUrl":"10.1016/j.eswa.2025.129830","url":null,"abstract":"<div><div>Recommender systems play a crucial role in our daily lives. However, in the context of high-value item recommendation, they face significant challenges. Due to the high price of these items, user purchase histories are often extremely sparse, making it difficult for recommender systems to accurately capture user preferences. Consequently, they tend to over-rely on popularity information. Moreover, the high-value item market exhibits a pronounced imbalanced distribution, where most user interactions focus on popular items. As a result, traditional recommender systems tend to prioritize these items while rarely recommending less popular ones, leading to low recommendation coverage. To address this challenge, we propose a <strong>P</strong>opularity-<strong>A</strong>ware <strong>D</strong>ebiasing (PAD) model, which improves recommendation coverage in high-value item scenarios without compromising accuracy. First, we employ soft prompts to guide a pre-trained language model (PLM) in enriching user representations. By incorporating semantic knowledge from the PLM, our model captures more comprehensive user preferences, ensuring recommendation accuracy while mitigating the model’s dependence on popularity signals. Building upon this, we apply popularity-aware debiasing to reduce overfitting and enhance coverage. PAD prevents the recommendation model from indiscriminately recommending the most popular items to all users, encouraging it to explore a wider range of items in its recommendations. Experiments conducted on industrial and public datasets demonstrate that our method mitigates popularity bias, significantly improving item recommendation coverage while maintaining accuracy.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129830"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221871","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":"Photovoltaic cluster power ultra-short-term cross-seasonal prediction integrating multi-channel information probabilistic diffusion generation and improved offset loss strategy","authors":"Mao Yang ,&nbsp;Yue Jiang ,&nbsp;Yunfeng Guo ,&nbsp;Jianfeng Che ,&nbsp;Wei He ,&nbsp;Kang Wu","doi":"10.1016/j.eswa.2025.129826","DOIUrl":"10.1016/j.eswa.2025.129826","url":null,"abstract":"<div><div>Accurate photovoltaic (PV) power prediction under complex meteorological conditions remains challenging, particularly given the pronounced seasonal variations that obscure generation patterns. This study presents a novel ultra-short-term prediction framework integrating meteorological volatility analysis with seasonal characteristic modeling. We developed a specialized multi-channel Gram angular summation field (MGASF) transformation matrix to holistically capture meteorological fluctuations, subsequently leveraging denoising diffusion probabilistic model (DDPM) for strategic augmentation of under-represented weather scenarios to enhance similar-day identification. Our hybrid architecture combines multi-channel vision Transformer (VIT) with bidirectional long and short-term memory (BILSTM) networks to synergistically analyze temporal dependencies and spatial patterns in PV similarity recognition. Furthermore, we engineered a seasonal-adaptive prediction system through an improved variable-weight Smooth L1 loss function, establishing an optimized seasonal alignment mechanism that achieves high-precision prediction across varying meteorological conditions with minimal computational overhead. Through rigorous validation using operational data from a utility-scale photovoltaic cluster in Western Inner Mongolia, the proposed method achieved consistent accuracy improvements: 3.02 % reduction in <em>N_RMSE</em>, 1.65 % decrease in <em>N_MAE</em>, and 2.19 % enhancement in R² compared to baseline approaches in PV cluster. These statistically significant enhancements demonstrate our framework’s capability to mitigate seasonal impacts while maintaining prediction reliability in complex meteorological environments.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129826"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158440","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":"Cohen’s class bilinear distributions and convolutional neural networks applied to broken rotor bar diagnosis","authors":"Avyner L.O. Vitor ,&nbsp;Alessandro Goedtel ,&nbsp;Wesley A. Souza ,&nbsp;Marcelo F. Castoldi ,&nbsp;Daniel Morinigo-Sotelo ,&nbsp;Oscar Duque-Perez","doi":"10.1016/j.eswa.2025.129835","DOIUrl":"10.1016/j.eswa.2025.129835","url":null,"abstract":"<div><div>Time-frequency (t-f) signal processing techniques are particularly advantageous for induction motor (IM) fault diagnosis under dynamic and variable industrial operating conditions. Broken rotor bar (BRB) faults remain among the most challenging to detect because of their proximity to the fundamental frequency and significantly lower amplitude in comparison. Additionally, traditional approaches often result in false positives or negatives in scenarios involving load variation, power quality issues, and inverter-fed operations. To address these issues, this work proposes a comprehensive and objective methodology to evaluate eight Cohen-Class Bilinear Distributions (CCBD) to diagnose BRB. CCBDs offer high-resolution t-f representations, a crucial advantage for fault identification. However, their use is limited by cross-terms, nonlinear artifacts inherent to bilinear processing. To overcome this limitation, convolutional neural networks (CNNs) are applied to automatically classify t-f images and identify the CCBD methods that effectively minimize the cross-terms while preserving fault signature harmonics. This strategy also avoids subjective and time-consuming visual inspections. In addition, this work proposes a novel CNN architecture with an attention module (CNN-Attention), designed to enhance performance in this context. The evaluation considers challenging conditions, including 1) line-fed and 2) inverter-fed operation, 3) voltage unbalance, and 4) load oscillations, applied to a 2 HP, 60 Hz motor. Generalization capability is validated with data collected from a different laboratory, using an independent 1 HP, 50 Hz motor and five different inverter models. Experimental results show that combining CNN-Attention with CCBDs enables highly accurate and fast classification, achieving approximately 96% accuracy even when trained and tested on distinct laboratory datasets, demonstrating the effectiveness and adaptability of the proposed method.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129835"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222086","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":"HWA-Net: Hierarchical window aggregate network for cross-resolution remote sensing change detection","authors":"Hualin Yang ,&nbsp;Boran Ren ,&nbsp;Zhijun Yang ,&nbsp;Jing Xiong ,&nbsp;Xiying Li ,&nbsp;Calvin Yu-Chian Chen","doi":"10.1016/j.eswa.2025.129829","DOIUrl":"10.1016/j.eswa.2025.129829","url":null,"abstract":"<div><div>Cross-resolution remote sensing change detection (CD) is a critical task in various applications, including urban monitoring, environmental changes, and disaster management, where images captured at different times often possess varying spatial resolutions. Current methods typically address this by resampling low-resolution (LR) images to high-resolution (HR) formats, but such image-level strategies lead to significant artifacts and misalignment in the change map. These imperfections not only reduce detection accuracy but also lead to misleading or false change identifications, resulting in incorrect or incomplete conclusions in time-sensitive applications, such as land-use change detection or disaster monitoring. To address these challenges, we propose the Hierarchical Window Aggregate Network(HWA-Net), a novel framework that directly operates on cross-resolution image pairs without preprocessing, aiming to accurately aggregate cross-resolution representations for robust CD. HWA-Net initially employed window-based feature extraction to produce scale-independent representations, subsequently transferring these features to layered decoding. This process effectively enhances detection accuracy across diverse resolutions. Our approach establishes new state-of-the-art results on three synthesized datasets and one real-world cross-resolution change detection dataset.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129829"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222082","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":"LLM-based exploration and analysis of real-time and historical blockchain data","authors":"S. Gebreab ,&nbsp;A. Musamih ,&nbsp;K. Salah ,&nbsp;R. Jayaraman","doi":"10.1016/j.eswa.2025.129851","DOIUrl":"10.1016/j.eswa.2025.129851","url":null,"abstract":"<div><div>Blockchain technology has revolutionized digital transactions and decentralized applications through its transparent and immutable ledger system, with platforms like Ethereum processing millions of transactions daily. However, as blockchain networks grow, traditional blockchain explorers show limitations when providing intuitive access to this vast data landscape, particularly when handling complex analytical queries, interpreting transaction patterns, and serving users without technical expertise. In this paper, we address these limitations by proposing an intelligent blockchain explorer that combines a Large Language Model (LLM)-powered agent for real-time blockchain interactions with a schema-aware SQL agent for historical data analysis. For real-time interactions, a dedicated blockchain agent connects to live networks through external APIs and specialized tools to process queries about current transactions and network states. When analyzing historical data patterns, we use an approach in which a Retrieval-Augmented Generation (RAG) system enhances the SQL agent’s understanding of the blockchain database schema and structure. This SQL agent subsequently translates natural language queries into SQL commands for efficient data retrieval from our periodically synchronized blockchain database. A query processor, powered by an LLM, intelligently routes user queries between these components based on temporal and contextual requirements, which enables both immediate blockchain state analysis and complex historical data querying. We evaluate our system on diverse blockchain queries, including complex analytical scenarios and multi-step operations. The experimental results demonstrate the effectiveness of our schema-aware SQL agent in accurate query translation and the overall system’s capability in handling both real-time and historical blockchain data exploration tasks.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129851"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222089","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":"Balancing semantic and structural decoding for fMRI-to-image reconstruction","authors":"Wanqi He ,&nbsp;Jin Wang ,&nbsp;Hui Li ,&nbsp;Hanyang Chi ,&nbsp;Bingfeng Zhang","doi":"10.1016/j.eswa.2025.129836","DOIUrl":"10.1016/j.eswa.2025.129836","url":null,"abstract":"<div><div>Reconstructing visual images from fMRI signals is an enticing task that opens new horizons in understanding the intricate workings of human cognition. Most existing methods benefit from the diffusion model to decode high-level semantic information from fMRI signals, achieving promising semantic reconstruction. However, such a solution ignores low-level structure information, <em>e.g.</em>, object location and color, leading to an uncompleted visual reconstruction. In this work, we present a novel fMRI-to-image approach to reconstruct high-quality images by balancing semantic and structural decoding in the diffusion model. Specifically, we first utilize the CLIP model and an MLP module to extract sufficient semantic information and structural details, respectively. Then we design a <strong>S</strong>emantic and <strong>S</strong>tructural <strong>A</strong>wareness <strong>B</strong>alanced module (<strong>SSAB</strong>) to predict the weight of structural information for the current denoising step, thus generating high-quality images by gradually integrating semantic and structural information during image reconstruction. Experimental results demonstrate that the proposed SSAB model is effective with only a few extra parameters, it achieves state-of-the-art performance in comprehensively evaluating both semantic and structural metrics. All code is available on <span><span>https://github.com/Venchy-he/SSAB</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129836"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145189554","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":"Group-guided prompt learning for vision-language models","authors":"Yufei Zheng ,&nbsp;Shengsheng Wang ,&nbsp;Yansheng Gao","doi":"10.1016/j.eswa.2025.129846","DOIUrl":"10.1016/j.eswa.2025.129846","url":null,"abstract":"<div><div>Prompt learning has become one of the mainstream approaches for enabling Vision-Language Models (VLMs) to effectively adapt to downstream tasks. Recent approaches enhanced the generalization of models by integrating prior knowledge from large language models (LLMs). However, these approaches overlook the potential value of group knowledge derived from semantic correlations across different classes, which may limit the performance of the model in the face of complex downstream tasks. To overcome this challenge, we propose <strong>Group-guided Prompt Learning (GGPL)</strong>, which integrates group knowledge into the original text prompts through LLMs. Specifically, GGPL uses LLMs to group all classes and integrates the group knowledge into the original text prompts to construct the final text prompts. Furthermore, we introduce a novel <strong>Group Knowledge Alignment (GKA)</strong> module, which aligns the learnable prompt features with the pre-trained features that contain group knowledge, preventing the learnable prompt features from feature shift during the training process and thus reducing overfitting. Experimental results across 11 public datasets demonstrate that the proposed GGPL method achieves significant improvement on various prompt learning approaches, while numerous ablation experiments also demonstrate the effectiveness of the each component of our GGPL method.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129846"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221873","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":"Multi-hop commonsense knowledge injection framework for zero-shot commonsense question answering","authors":"Xin Guan ,&nbsp;Jiuxin Cao ,&nbsp;Biwei Cao ,&nbsp;Qingqing Gao ,&nbsp;Bo Liu","doi":"10.1016/j.eswa.2025.129806","DOIUrl":"10.1016/j.eswa.2025.129806","url":null,"abstract":"<div><div>Zero-shot commonsense question answering (QA) task is to evaluate the general reasoning ability of the language model without training on the specific datasets. The existing zero-shot framework transforms triples within the commonsense knowledge graphs (KGs) into QA-format samples, serving as a pre-training data source to integrate commonsense knowledge into the language model. However, this approach still faces the following challenges: 1) The model trained from synthetic QA generated from triples lacks the multi-hop commonsense knowledge required for handling complex QA problems. 2) Ambiguity caused by confusing commonsense knowledge within synthetic QA, making it challenging for models to discern semantically similar entities. To address the above problem, we propose a novel <strong>M</strong>ulti-hop <strong>C</strong>ommonsense <strong>K</strong>nowledge <strong>I</strong>njection Framework (MCKI). Specifically, we draw inspiration from human complex reasoning thinking and further propose a synthetic multi-hop commonsense QA generation method. Meanwhile, we introduce negative samples with high confusion in synthetic QA, and then use contrastive learning to improve the model’s ability to distinguish similar commonsense knowledge. Extensive experiments on five commonsense question answering benchmarks demonstrate that our framework achieves state-of-the-art performance, surpassing existing methods, including large language models like GPT3.5 and ChatGPT.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129806"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222078","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":"Automated pothole detection and volume assessment using PointPSSN and smartphone LiDAR point clouds","authors":"Tingrui Zhang ,&nbsp;Xuequan Zhang ,&nbsp;Zichuan Yang ,&nbsp;Yumin Chen ,&nbsp;Li Song ,&nbsp;Weichen Zhang","doi":"10.1016/j.eswa.2025.129833","DOIUrl":"10.1016/j.eswa.2025.129833","url":null,"abstract":"<div><div>The rapid detection and assessment of potholes are critical for ensuring road traffic safety. However, point cloud-based techniques relying on surveying vehicles or drones are often expensive and may be limited by roadside obstruction or narrow roadways. This study proposes a novel approach for assessing road potholes using point cloud data collected by smartphone LiDAR. The method integrates the Point Pothole-Specialized Segmentation Network (PointPSSN), a lightweight point cloud segmentation model designed to achieve high accuracy with low parameter complexity and rapid inference, together with scale-adjustable voxelization for assessment. The PointPSSN model incorporates a Geometric Feature Encoder module to capture the geometric attributes of potholes by extracting local geometric features. Neighbor Finder module identifies and aggregates neighboring points that provide more significant information. Experiments were conducted using a smartphone LiDAR device within a 7.28 km² area of Wuchang District, Wuhan, China, encompassing diverse road conditions. A dataset of 1040 potholes was constructed for model training and evaluation. The results demonstrate that the PointPSSN model achieves a segmentation accuracy of 97.336 %, precision of 91.322 %, recall of 79.888 %, an F1-score of 85.223 %, and an intersection-over-union (IoU) of 74.251 %. Notably, the accuracy, F1-score, and IoU surpass the performance of state-of-the-art models by 0.233 %, 1.336 %, and 2.006 %, respectively. In terms of efficiency, PointPSSN requires only one-seventh of the FLOPs and one-fifteenth of the parameters of state-of-the-art models, while achieving an 18.37 % faster inference speed. Furthermore, the average relative errors in depth and volume assessment using voxelization methods are 9.08 % and 9.04 %, respectively.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129833"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222092","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 hybrid graph and LLM approach for measuring scientific novelty via knowledge recombination and propagation","authors":"Zhongyi Wang ,&nbsp;Zeren Wang ,&nbsp;Guangzhao Zhang ,&nbsp;Jiangping Chen ,&nbsp;Markus Luczak-Roesch ,&nbsp;Haihua Chen","doi":"10.1016/j.eswa.2025.129794","DOIUrl":"10.1016/j.eswa.2025.129794","url":null,"abstract":"<div><div>Scientific novelty constitutes a fundamental catalyst for both disciplinary innovation and interdisciplinary progress. Nevertheless, prevailing approaches to novelty assessment predominantly emphasize a single analytical dimension–either the semantic content of the focal paper or its cited references. Content-based methodologies frequently fail to incorporate the foundational knowledge cited by the target publication, whereas reference-based strategies tend to disregard the intrinsic conceptual contributions of the focal work itself. To address this limitation, the present study introduces a hybrid graph and large language model approach to jointly capture and integrate knowledge embedded in both the focal paper and its cited literature. The proposed method, which integrates knowledge recombination and propagation, is structured into four primary stages. First, prompt-based extraction techniques using general LLMs are applied to extract knowledge. Second, a Reference Knowledge Combination Network (RKCN) is constructed to model the knowledge referenced by the focal paper. Third, the RKCN is initialized with representations generated by SciDeBERTa(CS), and a graph attention network is employed to propagate knowledge across the network. Finally, the novelty of the focal paper is quantified by aggregating the novelty scores of all internal knowledge combinations based on the propagated representations. Experimental evaluation in the domain of artificial intelligence (AI) demonstrates that the proposed method significantly outperforms existing baseline approaches in quantifying scientific novelty. Additional ablation studies further validate the contribution of the knowledge propagation module. A case study illustrates the interpretability of our approach, and a cross-field validation in Biomedical Engineering (BME) domain highlights its robustness and cross-domain generalizability. A multi-dimensional comparative analysis between award-winning and non-award papers further reveals that the former generally incorporate a larger volume of knowledge and exhibit greater diversity in knowledge combinations. Moreover, while both groups encompass knowledge combinations spanning a wide range of novelty, award-winning papers display a stronger concentration at higher novelty levels, in contrast to the more uniform distribution observed in non-award papers. Data, code, and more detailed results are publicly available at: <span><span>https://github.com/haihua0913/graphLLM4ScientificNovelty</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129794"},"PeriodicalIF":7.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221478","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}