Artificial Intelligence Review最新文献

{"title":"Geniebrush: a text-guided multimodal image restoration and editing framework for complex scenes","authors":"Hongqing Wang,&nbsp;Jun Kit Chaw,&nbsp;Marizuana Mat Daud,&nbsp;Liantao Shi,&nbsp;Nannan Huang,&nbsp;Tin Tin Ting,&nbsp;Yong Wang","doi":"10.1007/s10462-026-11602-w","DOIUrl":"10.1007/s10462-026-11602-w","url":null,"abstract":"<div><p>We present GenieBrush, a text-guided multimodal framework for image restoration and editing in complex scenes. Unlike existing models that rely on generic diffusion backbones or static attention mechanisms, GenieBrush integrates language-conditioned reasoning, fine-grained localization, and dynamic refinement to achieve faithful and controllable visual editing. Specifically, we propose three novel modules: (1) Object-Conditioned Alignment Module (OCAM), which decomposes images into slot-based object representations and performs gated alignment with textual instructions; (2) Spatially-Preserving Instruction Network (SPIN), which generates discrete edit masks through vector-quantized alignment of vision-language embeddings, enabling precise target localization; and (3) Noise-Aware Refinement Engine (NARE), which dynamically adjusts the denoising schedule based on structural feedback to preserve fine textures and semantic integrity. Built upon Qwen2.5-VL and Stable Diffusion 1.5 with LoRA-based tuning, GenieBrush achieves state-of-the-art performance on MagicBrush and EMU Edit benchmarks. It surpasses recent baselines by 2.8% CLIP-I, reduces L1/L2 errors by 11.2%/14.1%, and exhibits superior DINO similarity. Extensive ablation studies validate the necessity of each proposed module. Our framework offers a generalizable and scalable solution for open-ended multimodal image editing tasks.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 10","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11602-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"A chaotic zebra optimization algorithm for numerical and constrained engineering applications: a case study on MLP classification challenges","authors":"Ardhala Bala Krishna,&nbsp;Vikram Kumar Kamboj,&nbsp;Arra Ganga Dinesh Kumar,&nbsp;Amit Kohli,&nbsp;Chaman Verma,&nbsp;Zoltán Illés","doi":"10.1007/s10462-026-11589-4","DOIUrl":"10.1007/s10462-026-11589-4","url":null,"abstract":"<div><p>Optimization search algorithms plays a crucial role in resolving complex constrained engineering and Multi Layer Perceptron (MLP) classification problems, often requiring efficacious and robust search strategies. This study introduces the Chaotic Zebra Optimization Algorithm (CZOA), an enriched version of the classical Zebra Optimization Algorithm (ZOA), incorporating chaotic maps to enhance search efficiency and convergence speed. The CZOA variants (CZOA1, CZOA2, CZOA3, and CZOA4) are evaluated using 23 standard benchmark functions across multiple dimensions (10D, 30D, 50D, and 100D), the CEC 2022 benchmark test suite, and three constrained engineering optimal design problems (three-bar truss, pressure vessel, and multi-disk clutch optimization). Furthermore, CZOA’s effectiveness is evaluated on four MLP classification datasets (Balloon, Iris, Breast Cancer, and XOR dataset). Simulation results reveal that CZOA1 consistently outperforms other variants and classical ZOA, achieving the lowest fitness values, lowered standard deviations, and enriched convergence rates. Specifically, in F1 (10D), CZOA1 reduces computation time from 4559.9 s (ZOA) to just 0.1563 s, achieving faster speed up. In CEC 2022 functions test suit, CZOA1 delivers the best performance with an average fitness of 333.86 and a standard deviation of 0.69, surpassing state-of-the-art metaheuristics such as GWO, WOA, and TLBO. Engineering optimal design results confirm CZOA1’s superior fitness values, with 263.8965 in the three-bar truss problem and 6849.485 in pressure vessel optimization, outperforming classical methods like GWO, DE, and PSO. For MLP classification tasks, CZOA1 achieves a 99.00% classification rate in breast cancer detection, highlighting its superior ability in handling high-dimensional search spaces. Comparative evaluations demonstrate that chaotic maps significantly enhance exploration–exploitation balance, reduce computational search complexity, and improve solution accuracy. The findings establish CZOA as a promising optimization technique for real-world engineering and classification challenges.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 10","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11589-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Exploring the architectures of large language models and impact across multiple domains: a review","authors":"Muhammad Waqas,&nbsp;Ruqiya Rajab,&nbsp;Asif Ali Laghari,&nbsp;Natalia Kryvinska,&nbsp;Abdullah Al Hejaili,&nbsp;Noman Khan,&nbsp;Mansoor Ahmed Khuhro,&nbsp;Fengli Zhang","doi":"10.1007/s10462-026-11609-3","DOIUrl":"10.1007/s10462-026-11609-3","url":null,"abstract":"<div><p>Large language models (LLMs) have become a major focus of research in natural language processing due to their high-quality performance in diverse language-based tasks. Built on deep learning methods like transformers, these models are trained on vast amounts of text datasets, enabling them to handle a wide range of language tasks with impressive accuracy. This review paper examines the factors that make LLMs so effective and their applications across various fields, including healthcare, education, software development, and customer service. This review synthesizes literature published between 2017 and 2025 and examines the evolution of LLMs from early transformer-based systems to contemporary families, including bidirectional encoder representations from transformers, generative pretrained transformer, and text-to-text transfer transformer (T5). This review paper also explains the key components of their design, such as pretraining to learn general language patterns, fine-tuning for specific tasks, attention mechanisms that help models focus on important words, and reinforcement learning from human feedback to align outputs with human expectations. LLMs are automating tasks to enhance user interactions and support better decision-making with data. However, they face challenges, including biased data, errors, ethical concerns, privacy risks, and high computational costs. This review paper also introduces a unifying analytic framework based on model capacity, signal design, and context externalization to explain when different LLM paradigms tend to succeed or fail, and offers comparative, task-aligned matrices and domain-specific mappings that convert disparate findings into an actionable agenda. Rather than making universal performance claims, this review emphasizes that outcomes vary across benchmarks, datasets, and deployment settings. The paper concludes by outlining future research directions in model efficiency, grounding, evaluation, and responsible deployment and researchers must focus on maintaining fairness, clarity, and accountability.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 10","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11609-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Ranging from prediction to planning via machine learning approaches for autonomous driving: a survey","authors":"Ding Li,&nbsp;Qichao Zhang,&nbsp;Dongbin Zhao","doi":"10.1007/s10462-026-11604-8","DOIUrl":"10.1007/s10462-026-11604-8","url":null,"abstract":"<div><p>Accurately predicting the future trajectories of surrounding agents and planning a safe, comfortable trajectory for the automated vehicle (AV) are of utmost importance for autonomous driving systems (ADS). In particular, the rise of machine learning-driven interactive prediction and planning frameworks has revolutionized this field. Existing autonomous driving systems, such as modular stacks and end-to-end frameworks, remain challenges as they commonly model the prediction and planning sequentially, neglecting the dynamic reactions of surrounding agents to the planning behaviors of the AV. Instead, interactive prediction and planning frameworks benefit from bi-directional interactive modeling between the AV and surrounding agents in a joint optimization manner, circumventing the drawbacks associated with existing autonomous driving systems and boosting safety and comfortable transportation. Through gradual modeling paradigms ranging from marginal prediction, conditional prediction, and interactive prediction and planning, this paper delves into fundamental principles and the growth journey of the ultimate target for autonomous driving. Built upon this, we systematically present a comprehensive review of state-of-the-art papers, encompassing the methodology, experimental platforms, and evaluation criteria during the development of interactive prediction and planning. Additionally, this paper details critical challenges concerning scene comprehension, planning safety, robustness, generalization and deployment. Accordingly, we discuss current advancements such as hot topics of large language models (LLMs), world models, distillation strategies, reinforcement learning from human feedback (RLHF), and data balance and long-tail learning, finally proposing future promising directions to inspire continued innovation and exploration.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 10","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11604-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Grey wolf optimizer and whale optimization algorithm: a systematic review","authors":"Abdelmawla Yousef,&nbsp;Rahma Ezzat,&nbsp;Nora El-Rashidy","doi":"10.1007/s10462-026-11585-8","DOIUrl":"10.1007/s10462-026-11585-8","url":null,"abstract":"<div><p>Metaheuristic algorithms that take inspiration from nature’s processes are frequently utilized for solving very complicated optimization problems due to their flexibility and efficiency. Within the context of this, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA) have become two of the most popular metaheuristics since they have a relatively simple structure along with good performance characteristics. However, both algorithms suffer from similar limitations, such as premature convergence and not having a good balance between their ability to explore the search space and their ability to exploit good locations in the search space. These limitations have led many researchers to develop new and enhanced versions of the GWO and WOA algorithms. Although there have been many papers published on these topics due to the exponential increase in research activity surrounding this area of study, there currently exists a gap between researchers who have published enhancements to these algorithms and researchers who would like to build upon or further develop enhancements to these algorithms. The purpose of this paper is to provide researchers with a complete and structured overview of the most recent advances in GWO and WOA. Thus, we utilized five academic databases (ScienceDirect, SpringerLink, IEEE Xplore, and ACM Digital Library) for gathering different researches and a modified version of the PRISMA method has been used for the identification, screening, and analysis of recent papers that contain improvements to the GWO or WOA algorithms. We categorize the identified papers based on their improvement methodology, hybridization strategy, and application area. Finally, we identify some current research opportunities and describe a few high-priority areas of future research activities.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 7","pages":""},"PeriodicalIF":13.9,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11585-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148172217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Addressing data scarcity with Few-Shot Learning: a systematic literature review","authors":"Inas Lamtaai,&nbsp;Basma Ben Jilali,&nbsp;Ikram Chairi,&nbsp;Abdelouahid Lyhyaoui","doi":"10.1007/s10462-026-11584-9","DOIUrl":"10.1007/s10462-026-11584-9","url":null,"abstract":"<div><p>Recent advancements in Machine Learning and Deep Learning have yielded remarkable achievements across a wide range of fields, including object detection, image classification, and Natural Language Processing (NLP). This progress has been largely driven by the availability of large-scale annotated datasets, which enable high accuracy and strong generalization. However, such performance levels remain out of reach in settings characterized by limited data and high annotation costs. This challenge has motivated the emergence of Few-Shot Learning (FSL), a novel branch of artificial intelligence that seeks to replicate the human brain’s capacity to learn effectively from minimal examples. This systematic literature review offers a comprehensive examination of FSL techniques, drawing on relevant research, spanning from 2015 to 2025. The reviewed works are organized according to their learning strategies for data-limited settings, covering meta-learning models, hybrid models, and non-meta learning approaches, alongside a survey of notable real-world applications where FSL has been successfully deployed. Beyond exploring the progress within this field of research, this review advances several critical and underexplored findings. First, the evidence accumulated across the reviewed literature suggests that representation quality consistently outweighs algorithmic sophistication as the primary driver of performance gains, a finding with direct implications for how future FSL research should prioritize its efforts. Second, the episodic training paradigm, which underlies a large body of FSL methodology, exhibits structural misalignments with real-world deployment conditions. Third, the field exhibits a systematic bias toward natural image benchmarks, which constrains the transferability of algorithmic advances to other critical domains that operate on different data modalities. Fourth, the dominant evaluation practice of averaged accuracy across tasks is shown to be an inadequate proxy for practical deployment performance. Together, these limitations reveal important structural gaps in the current FSL landscape. In light of these findings, the review identifies concrete avenues for improvement aimed at bridging these gaps, enhancing model robustness, and ultimately enabling more reliable FSL deployment in real-world applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 9","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11584-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Visual enhancement and 3D representation for underwater scenes: a review","authors":"Guoxi Huang,&nbsp;Haoran Wang,&nbsp;Brett Seymour,&nbsp;Evan Kovacs,&nbsp;John Ellerbroc,&nbsp;Dave Blackham,&nbsp;Nantheera Anantrasirichai","doi":"10.1007/s10462-026-11597-4","DOIUrl":"10.1007/s10462-026-11597-4","url":null,"abstract":"<div><p>Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in computer vision and AI-based tasks due to complex imaging conditions in aquatic environments. Despite the development of numerous enhancement algorithms, a comprehensive and systematic review covering both UVE and underwater 3D reconstruction remains absent. To advance research in these areas, we present an in-depth review from multiple perspectives. First, we introduce the fundamental physical models, highlighting the peculiarities that challenge conventional techniques. We survey advanced methods for visual enhancement and 3D reconstruction specifically designed for underwater scenarios. The paper assesses various approaches from non-learning methods to advanced data-driven techniques, including Neural Radiance Fields and 3D Gaussian Splatting, discussing their effectiveness in handling underwater distortions. Finally, we conduct both quantitative and qualitative evaluations of state-of-the-art UVE and underwater 3D reconstruction algorithms across multiple benchmark datasets. Finally, we highlight key research directions for future advancements in underwater vision.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 10","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11597-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"From extractive to generative: multi-document summarization in the era of generative AI - advances, challenges, and emerging trends","authors":"Balamurugan Palanisamy,&nbsp;G. S. S. Chalapathi,&nbsp;Vikas Hassija","doi":"10.1007/s10462-026-11580-z","DOIUrl":"10.1007/s10462-026-11580-z","url":null,"abstract":"<div><p>Multi-Document Summarization (MDS) has evolved from extractive techniques to sophisticated generative models, driven by advances in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), diffusion-based generation, and graph-enhanced architectures. While these methods have significantly improved the coherence, informativeness, and factuality of summaries, the growing diversity of approaches has introduced challenges in understanding their design trade-offs, domain applicability, and evaluation. This survey provides a comprehensive review of generative MDS models, offering a structured taxonomy that categorizes them into LLM-based, RAG-enhanced, graph-augmented, and diffusion-based families. We analyze each category’s architectural principles, capabilities, and limitations. In addition, we highlight benchmark datasets, evaluation protocols, and key application domains, including healthcare, legal, and scientific summarization. To move beyond descriptive comparison, we propose a unified analytical framework that decomposes generative MDS into three core dimensions: information aggregation, structural reasoning, and factual grounding. This framework organizes prior work, clarifies architectural trade-offs, and connects current limitations to actionable future research directions. Finally, we discuss open challenges such as hallucination, domain adaptation, and evaluation bottlenecks, and propose future research directions. This survey aims to guide researchers and practitioners in navigating the rapidly evolving landscape of trustworthy and scalable generative MDS.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11580-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"AI in cardiovascular disease classification—a systematic literature review","authors":"Manali Karandikar,&nbsp;Suraj Sawant,&nbsp;Satish Kumbhar,&nbsp;Ranjeet Bidwe","doi":"10.1007/s10462-026-11592-9","DOIUrl":"10.1007/s10462-026-11592-9","url":null,"abstract":"<div><p>Despite advances in integrating Artificial Intelligence (AI) with Electrocardiogram (ECG) analysis, several limitations remain, including limited dataset diversity, class imbalance, underrepresentation of rare conditions, and lack of external validation, which affect generalizability and minority class performance. Many approaches rely on “black-box” models with limited interpretability, while deep and hybrid architectures can be computationally expensive, and multimodal integration remains constrained. Following the PRISMA methodology, 490 records were retrieved, 223 studies were selected after screening, and four additional studies were included through snowballing, resulting in a total of 227 studies. This review addresses whether multimodal AI improves prediction over single-modality approaches, how Explainable AI (XAI) supports interpretability, and how self-supervised, transformer, and transfer learning models compare with traditional methods. Findings show that multimodal approaches frequently show improved performance, with many studies reporting gains of 2–6% even though results vary across datasets and tasks. Advanced architectures can extract complex features, but their effectiveness depends on data quality and evaluation metrics. XAI methods give context-dependent support for interpretability, while self-supervised and transfer learning approaches show potential in low-label settings. Future research should focus on lightweight models, standardized evaluation, and improved clinical validation. This review highlights emerging trends in multimodal learning, XAI, and advanced architectures in ECG-based CVD analysis.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 9","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11592-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Recent advances in music generation: methods, evaluation, and challenges","authors":"Wei Duan,&nbsp;Yi Yu,&nbsp;Jean-Pierre Briot","doi":"10.1007/s10462-026-11582-x","DOIUrl":"10.1007/s10462-026-11582-x","url":null,"abstract":"<div><p>Music generation has been an active research topic in AI for music, drawing considerable attention from both the academic community and industry. Recent advances in deep learning, coupled with the growing availability of diverse music data, have driven the development of innovative approaches in this field. Therefore, this paper aims to provide an overall survey of music generation, spanning from statistical models and neural network architectures to the pre-training and fine-tuning of large language models (LLMs). This survey adopts a broad view of music generation covering symbolic, audio, and multimodal settings, as well as autonomous, interactive, and co-creative generation. It further examines multiple aspects in depth, including key challenges, datasets, generative frameworks, and objective evaluation metrics. Most importantly, we synthesize recent progress in music generation and highlight promising directions for future research. By consolidating these perspectives, this survey highlights unique features that align with current trends in music generation and is expected to provide a valuable reference for advancing research in the field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 9","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11582-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}