Applied Soft Computing最新文献

{"title":"Long-short Term Cross Echo State Network for time series forecasting task","authors":"Dongchen Jiang ,&nbsp;Li Cui ,&nbsp;Yi Zeng ,&nbsp;Meiming You ,&nbsp;Guoqiang Wang","doi":"10.1016/j.asoc.2025.112997","DOIUrl":"10.1016/j.asoc.2025.112997","url":null,"abstract":"<div><div>Investigating the dynamics of time series in nonlinear systems has become a prominent research focus in both theoretical and practical domains. Unveiling the intrinsic characteristics of nonlinear time series can significantly enhance the understanding and modelling of nonlinear systems. Among the various time prediction models, Reservoir Computing (RC) has garnered widespread attention due to its distinctive hidden layer architecture. The Echo State Network (ESN) is one of the most representative instances within the RC framework. However, most existing ESNs do not explicitly capture the fixed multi-scale dependencies in time series, and their short-term memory (STM) cannot meet the needs of specific time series. To address these limitations, this paper introduces a novel Echo State Network with a heterogeneous topology, named the Long-short Term Cross Echo State Network (LS-CrossESN). The overall architecture of this model consists of three different types of reservoirs in parallel. And it incorporates a heterogeneous topology structure known as the cross architecture, which merges those of the first reservoir with the state characteristics of the second reservoir, so that the information between the two reservoirs can be transmitted to each other. At the same time, a time-delay operator is inserted in the second reservoir, so that the fused characteristics would not be immediately input to the next layer but transmitted to the deep layer. In this way, the characteristics of input would not decay with the update of the layers. The structure of third reservoir captures the influence of recent historical memory through a specific sliding window technology, and finally the multi-scale states from each layer would be collected for combined prediction. To optimize parameters in this model, an Improved Salp Swarm Algorithm (ISSA) is proposed. The model was tested of eight datasets spanning three categories: Mackey-Glass series, Lorenz chaotic series, Sunspot series, airport temperature series, and two real network traffic datasets. The experimental results demonstrate that the STM of LS-CrossESN is significantly improved compared with Deep-ESN, LS-ESN, DATDR and ADRC. Across all eight datasets, the model exhibits robust performance in both one-step-ahead and multi-step predictions.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112997"},"PeriodicalIF":7.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687355","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":"Domain adaptive person re-identification with noise optimization and dynamic weighting","authors":"Zhengyang Wang ,&nbsp;Xiufen Ye ,&nbsp;Xue Shang ,&nbsp;Shuxiang Guo","doi":"10.1016/j.asoc.2025.112932","DOIUrl":"10.1016/j.asoc.2025.112932","url":null,"abstract":"<div><div>Domain adaptive person re-identification (Re-ID) faces challenges due to inherent noise from limited domain transferability and the uncertainty in pseudo-label generation. To address this, we propose NODW (Noise Optimization and Dynamic Weighting), a comprehensive domain adaptive person Re-ID framework that systematically tackles these issues through quantitative noise assessment and dynamic optimization. Our method proposes: (1) an enhanced ResNet50-pro backbone specifically designed for cross-domain feature extraction, (2) a silhouette coefficient-based module for pseudo-label quality assessment with dynamic weighting, (3) a Maximum Mean Discrepancy (MMD)-based module for minimizing domain transferability limitations, and (4) a robust consistency supervision mechanism to ensure stable feature learning. Extensive experiments demonstrate state-of-the-art performance across multiple domain transfer tasks, achieving mAP scores of 73.8% (Market to Duke), 84.7% (Duke to Market), 34.2% (Market to MSMT), and 35.6% (Duke to MSMT). These results represent significant improvements over existing methods, particularly in challenging scenarios with large domain gaps, validating the effectiveness of our noise-aware adaptation strategy.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112932"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610992","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":"Mindful Human Digital Twins: Integrating Theory of Mind with multi-agent reinforcement learning","authors":"Luis Zhinin-Vera,&nbsp;Elena Pretel,&nbsp;Víctor López-Jaquero,&nbsp;Elena Navarro,&nbsp;Pascual González","doi":"10.1016/j.asoc.2025.112939","DOIUrl":"10.1016/j.asoc.2025.112939","url":null,"abstract":"<div><div>Multi-Agent Reinforcement Learning (MARL) is focused on enabling autonomous agents to learn and adapt to complex environments through interactions with their surroundings and other agents. A key challenge in MARL is developing agents with the human-like capacity to understand, predict, and respond to the intentions and mental states of their peers. This capability, commonly referred to as the Theory of Mind (ToM), is central to fostering more sophisticated and realistic interactions among autonomous agents. In this paper, we propose a novel approach that leverages Theory-Theory (TT) and Simulation-Theory (ST) to enhance ToM within the MARL framework. Building on the Digital Twins (DT) framework, we introduce the Mindful Human Digital Twin (MHDT). These intelligent systems enriched with ToM capabilities bridge the gap between artificial agents and human-like interactions. In this work, we utilized OpenAI Gymnasium to perform simulations and evaluate the effectiveness of our approach. This work represents a significant step forward in Artificial Intelligence (AI), resulting in socially intelligent systems capable of natural and intuitive interactions with both their environment and other agents. This approach is particularly effective in addressing critical social challenges such as school bullying. This research not only advances the growing field of MARL but also paves the way for sophisticated AI systems with enhanced ToM abilities, tailored for complex and sensitive real-world applications.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112939"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610993","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 neural priority model for agile earth observation satellite scheduling using deep reinforcement learning","authors":"Ming Chen ,&nbsp;Luona Wei ,&nbsp;Jie Chun ,&nbsp;Lei He ,&nbsp;Shang Xiang ,&nbsp;Lining Xing ,&nbsp;Yingwu Chen","doi":"10.1016/j.asoc.2025.112984","DOIUrl":"10.1016/j.asoc.2025.112984","url":null,"abstract":"<div><div>The agile earth observation satellite scheduling problem (AEOSSP) is a time-dependent and complex combinatorial optimization challenge that has spurred extensive research for decades. Traditional methods have primarily relied on iterative searching processes to approximate near-optimal solutions, but their efficiency remains limited. To address this issue, we propose a Priority Construction Model (PCM) based on deep reinforcement learning (DRL), forming a learning-based, two-stage construction heuristic. The PCM integrates a Priority Construction Neural Network (PCNN) alongside a Backward-Slacken and Top-Insert (BS-TI) scheduling algorithm. In PCM, the PCNN sequences observation requests, while the BS-TI schedules each sequenced request in accordance with specific constraints, thus freeing the neural policy from the burden of complex constraint checking. Experimental results indicate that following a policy-gradient-based DRL training process, PCM outperforms the state-of-the-art AEOSSP iterative algorithm, achieving better average profits within an exceptionally short construction time in most scenarios. The model study further reveals that PCNN outperforms other DRL policies in terms of priority policy representation, while the PCM exhibits superior generalization capabilities across varying scales and distributions. Therefore, our proposed model presents a valuable reference solution that not only meets the large-scale and rapid response requirements of the AEOSSP but also holds potential for application in upcoming large constellations and emerging management paradigms. More importantly, we introduce a novel framework that separates the DRL optimization process from constraint management, lowering the entry barrier for applying DRL to complex problems. This makes the model adaptable to various optimization challenges in engineering and operations research, thus extending its applicability beyond the AEOSSP domain.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112984"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601315","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":"Generative fuzzer-driven vulnerability detection in the Internet of Things networks","authors":"Mohammed Tanvir Masud,&nbsp;Nickolaos Koroniotis,&nbsp;Marwa Keshk,&nbsp;Benjamin Turnbull,&nbsp;Shabnam Kasra Kermanshahi,&nbsp;Nour Moustafa","doi":"10.1016/j.asoc.2025.112973","DOIUrl":"10.1016/j.asoc.2025.112973","url":null,"abstract":"<div><div>The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, driving innovations such as Industry 4.0 and the creation of smart environments that enhance efficiency and asset management and enable intelligent decision-making. However, these benefits come with considerable cybersecurity risks due to inherent vulnerabilities within IoT ecosystems. Introducing potentially vulnerable IoT devices into secure environments, like smart airports, introduces new attack surfaces and vectors for exploitation. Identifying such vulnerabilities is challenging, and while traditional methods like penetration testing and vulnerability identification offer solutions, they often fall short due to IoT’s unique data diversity, hardware constraints, and complexity. We propose an intelligent mutation-based fuzzer for IoT vulnerability detection in networks to address these limitations, demonstrated through a smart airport case study. This method leverages Generative Adversarial Network (GAN)-based mutation, utilizing legitimate network communications (i.e., payloads) to produce fuzzed payloads that expose vulnerabilities. Additionally, we incorporate a large language model (LLM)-based risk assessment framework to evaluate the likelihood and impact of identified vulnerabilities, which is crucial for effectively prioritizing threats in interconnected IoT environments. This dual approach of vulnerability detection and LLM-driven risk assessment provides comprehensive insights into IoT security, enabling prioritized response actions. Experiments conducted in the UNSW Canberra IoT testbed confirm that our approach outperforms conventional vulnerability identification methods, offering a scalable solution for effective vulnerability detection and risk prioritization in complex IoT networks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112973"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642506","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":"Hyperspectral image classification based on ConvGRU and spectral–spatial joint attention","authors":"Ronghua Shang ,&nbsp;Jie Yang ,&nbsp;Jie Feng ,&nbsp;Yangyang Li ,&nbsp;Songhua Xu","doi":"10.1016/j.asoc.2025.112949","DOIUrl":"10.1016/j.asoc.2025.112949","url":null,"abstract":"<div><div>In hyperspectral image classification, methods based on spectral–spatial joint attention mechanisms have demonstrated the ability to effectively enhance feature extraction. However, existing approaches still face limitations: spectral attention mechanisms often lack local–global feature interaction, spatial attention fails to fully exploit multi-scale information, and the joint modeling of spectral and spatial features remains insufficiently explored. To address these issues, this paper proposes a spectral–spatial joint attention network based on Convolutional Gated Recurrent Units (ConvGRU). First, a Local-Global Spectral Attention (LGSA) mechanism is designed, where one-dimensional convolution extracts local spectral features and fully connected layers enable global feature interaction. Second, a Multi-Scale Spatial Attention (MSSA) mechanism is introduced, employing three convolutional branches with different receptive fields to capture spatial features, followed by hierarchical feature fusion via 1 × 1 convolution. Finally, a channel-level feature fusion strategy based on ConvGRU is proposed, leveraging sequence modeling to achieve channel-wise joint enhancement of LGSA and MSSA, thereby enabling deep coupling of spectral and spatial features. Comparative experiments on three public datasets demonstrate that the proposed method outperforms seven state-of-the-art algorithms in terms of classification performance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112949"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620232","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":"LOSS-GAT: Label propagation and one-class semi-supervised graph attention network for fake news detection","authors":"Batool Lakzaei,&nbsp;Mostafa Haghir Chehreghani,&nbsp;Alireza Bagheri","doi":"10.1016/j.asoc.2025.112965","DOIUrl":"10.1016/j.asoc.2025.112965","url":null,"abstract":"<div><div>In today’s world of social networks, fake news spreads quickly and causes serious problems. This has made it crucial to develop automated systems to detect and combat disinformation. Machine learning and deep learning are often used to identify fake news, but they struggle due to the lack of labeled news datasets. To address this, the One-Class Learning (OCL) approach uses a small set of labeled data. On the other hand, representing data as a graph enables access to diverse content and structural information, and label propagation methods on graphs can be effective in predicting node labels. In this paper, we adopt a graph-based model for data representation and introduce a semi-supervised and one-class approach for fake news detection, called LOSS-GAT. Initially, we employ a two-step label propagation algorithm, utilizing Graph Neural Networks (GNNs) as an initial classifier to categorize news into two groups: interest (fake) and non-interest (real). Subsequently, we enhance the graph structure using structural augmentation techniques. Ultimately, we predict the final labels for all unlabeled data using a GNN that induces randomness within the local neighborhood of nodes through the aggregation function. We evaluate our proposed method on six common datasets and compare the results against a set of baseline models, including both OCL and binary labeled models. The results demonstrate that LOSS-GAT achieves a significant improvement in performance, with enhancements ranging from 5% (on the FEVER dataset) to 20% (on the FakeNewsNet dataset) in terms of the Macro-F1 metric, all while utilizing only a limited set of labeled fake news data. Noteworthy, LOSS-GAT even outperforms binary labeled models.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112965"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620231","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":"Dynamic environment adaptive online learning with fairness awareness via dual disentanglement","authors":"Qiuling Chen,&nbsp;Ayong Ye,&nbsp;Chuan Huang,&nbsp;Fengyu Wu","doi":"10.1016/j.asoc.2025.112975","DOIUrl":"10.1016/j.asoc.2025.112975","url":null,"abstract":"<div><div>The widespread application of Artificial Intelligence (AI) comes with the necessity to consider and mitigate discrimination in machine learning algorithms. Most existing fair machine learning methods are only suitable for short-term and static scenarios, and thus cannot adapt to dynamically changing environments or meet the needs for real-time updates. In open dynamic scenarios, data arriving in batches needs processing in real-time, and the constantly changing environment will lead to data distribution shifts, making it difficult to ensure the fairness of models in the long run. To achieve long-term fairness of models, we propose an online dual disentanglement method that captures fair representations of non-sensitive core information in real-time within constantly changing environments, thereby enhancing the robustness of fair models. Firstly, learned representations are disentangled from environment-specific variation factors through a constrained optimization setup to ensure semantic invariance. Further, a bias disentanglement method based on supervised contrastive learning is designed. While keeping the non-sensitive core information unchanged, the sensitive information is hidden from semantic representations and the spurious correlation with target labels is cut off, so as to achieve the long-term fairness of the model decision. By formulating the fairness-aware online learning problem in dynamic environments as an online optimization problem with the long-term fairness constraint, and theoretically proving that the algorithm achieves sublinear dynamic regret and sublinear violation of cumulative unfairness under certain assumptions. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed method, which maintains overall fairness above 80% without compromising utility, outperforming state-of-the-art baseline methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112975"},"PeriodicalIF":7.2,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601316","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":"AdvGrid: A multi-view black-box attack on infrared pedestrian detectors in the physical world","authors":"Kalibinuer Tiliwalidi ,&nbsp;Chengyin Hu ,&nbsp;Guangxi Lu ,&nbsp;Ming Jia ,&nbsp;Weiwen Shi","doi":"10.1016/j.asoc.2025.112981","DOIUrl":"10.1016/j.asoc.2025.112981","url":null,"abstract":"<div><div>Physical adversarial attacks in the visible spectrum have been extensively studied, but research on infrared attacks remains limited. Infrared pedestrian detectors are crucial for modern applications yet vulnerable to adversarial attacks, posing significant security risks. Existing methods using physical perturbations like light bulb arrays or hot/cold patches for black-box attacks have shown limitations in practicality and multi-view support. To address these challenges, we introduce Adversarial Infrared Grid (AdvGrid), a novel approach that models perturbations in a grid format and employs a genetic algorithm for black-box optimization. AdvGrid cyclically applies perturbations to various parts of a pedestrian’s clothing, enabling effective multi-view black-box attacks on infrared detectors. Our extensive experiments demonstrate AdvGrid’s superior performance: Effectiveness: Achieves 80.00% attack success rate in digital environments and 91.86% in physical environments. Stealthiness: Maintains high stealthiness, making it difficult for observers to identify the adversarial patterns. Robustness: Exceeds 50% average attack success rate against mainstream detectors, showcasing its robustness across different scenarios. We also conduct ablation studies, transfer attacks, and adversarial defense evaluations, further confirming AdvGrid’s superiority over baseline methods. Our findings highlight AdvGrid as a powerful tool for advancing the understanding and mitigation of adversarial threats in infrared detection systems.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112981"},"PeriodicalIF":7.2,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610991","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":"Dynamic light optimization in vertical farming using an IoT-driven digital twin framework and artificial intelligence","authors":"Rafael Gomes Alves ,&nbsp;Fábio Lima ,&nbsp;Ítalo Moraes Rocha Guedes ,&nbsp;Salvador Pinillos Gimenez","doi":"10.1016/j.asoc.2025.112985","DOIUrl":"10.1016/j.asoc.2025.112985","url":null,"abstract":"<div><div>The global agricultural sector faces mounting challenges from climate change, population growth, urbanization, and environmental degradation, necessitating innovative solutions to ensure food security. Urban and peri-urban agriculture, particularly vertical farming, offers a sustainable approach to increase food production while minimizing land use, reducing environmental impact, and enhancing resource efficiency. Unlike conventional vertical farming systems that rely on static spectral recipes with fixed light compositions (e.g., Red-to-Blue ratios derived from historical data), this study introduces an Internet of Things-enabled smart vertical farming system that leverages digital twin technology and a genetic algorithm (GA) to dynamically optimize lettuce growth by adjusting RGB LED spectra throughout the crop cycle. The system monitors and controls key environmental parameters within a growth tower, including temperature, humidity, and lighting. A digital twin facilitates real-time data exchange between physical and virtual components, while the GA iteratively refines the light composition. Over a 34-day cultivation period, the algorithm identified an optimal RGB configuration (R:211, G:169, B:243; maximum intensity: 255) that aligns with spectral values reported in literature for lettuce, despite not directly measuring photobiological metrics such as Photosynthetic Photon Flux Density. To our knowledge, this is the first study to implement a dynamic, GA-driven spectral optimization strategy in vertical farming. While the objective was not to surpass traditional static lighting recipes, the results validate that adaptive methods can reliably converge to established optima. The IoT platform demonstrated robust capabilities in data collection, processing, and actuation, underscoring the promise of adaptive lighting strategies for controlled agriculture. Future research will focus on incorporating additional spectra (e.g., deep red, ultraviolet), automating data collection via image recognition, and analyzing energy efficiency to enhance scalability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112985"},"PeriodicalIF":7.2,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610998","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}