International Journal of Intelligent Systems最新文献

{"title":"Veracity-Oriented Context-Aware Large Language Models–Based Prompting Optimization for Fake News Detection","authors":"Weiqiang Jin,&nbsp;Yang Gao,&nbsp;Tao Tao,&nbsp;Xiujun Wang,&nbsp;Ningwei Wang,&nbsp;Baohai Wu,&nbsp;Biao Zhao","doi":"10.1155/int/5920142","DOIUrl":"https://doi.org/10.1155/int/5920142","url":null,"abstract":"<div>\u0000 <p>Fake news detection (FND) is a critical task in natural language processing (NLP) focused on identifying and mitigating the spread of misinformation. Large language models (LLMs) have recently shown remarkable abilities in understanding semantics and performing logical inference. However, their tendency to generate hallucinations poses significant challenges in accurately detecting deceptive content, leading to suboptimal performance. In addition, existing FND methods often underutilize the extensive prior knowledge embedded within LLMs, resulting in less effective classification outcomes. To address these issues, we propose the CAPE–FND framework, context-aware prompt engineering, designed for enhancing FND tasks. This framework employs unique veracity-oriented context-aware constraints, background information, and analogical reasoning to mitigate LLM hallucinations and utilizes self-adaptive bootstrap prompting optimization to improve LLM predictions. It further refines initial LLM prompts through adaptive iterative optimization using a random search bootstrap algorithm, maximizing the efficacy of LLM prompting. Extensive zero-shot and few-shot experiments using GPT-3.5-turbo across multiple public datasets demonstrate the effectiveness and robustness of our CAPE–FND framework, even surpassing advanced GPT-4.0 and human performance in certain scenarios. To support further LLM–based FND, we have made our approach’s code publicly available on GitHub (our CAPE–FND code: https://github.com/albert-jin/CAPE-FND [Accessed on 2024.09]).</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5920142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115307","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":"Exposing the Forgery Clues of DeepFakes via Exploring the Inconsistent Expression Cues","authors":"Jiatong Liu,&nbsp;Lina Wang,&nbsp;Run Wang,&nbsp;Jianpeng Ke,&nbsp;Xi Ye,&nbsp;Yadi Wu","doi":"10.1155/int/7945646","DOIUrl":"https://doi.org/10.1155/int/7945646","url":null,"abstract":"<div>\u0000 <p>The pervasive prevalence of DeepFakes poses a profound threat to individual privacy and the stability of society. Believing the synthetic videos of a celebrity and trumping up impersonated forgery videos as authentic are just a few consequences generated by DeepFakes. We investigate current detectors that blindly deploy deep learning techniques that are not effective in capturing subtle clues of forgery when generative models produce remarkably realistic faces. Inspired by the fact that synthetic operations inevitably modify the regions of eyes and mouth to match the target face with the identity or expression of the source face, we conjecture that the continuity of facial movement patterns representing expressions that existed in the veritable faces will be disrupted or completely broken in synthetic faces, making it a potentially formidable indicator for DeepFake detection. To prove this conjecture, we utilize a dual-branch network to capture the inconsistent patterns of facial movements within eyes and mouth regions separately. Extensive experiments on popular FaceForensics++, Celeb-DF-v1, Celeb-DF-v2, and DFDC-Preview datasets have demonstrated not only effectiveness but also the robust capability of our method to outperform the state-of-the-art baselines. Moreover, this work represents greater robustness against adversarial attacks, achieving ASR of 54.8% in the I-FGSM attack and 43.1% in the PGD attack on the DeepFakes dataset of FaceForensics++, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7945646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114897","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":"Research on the Digital Twin Polder Area System Driven by Integrating the Xin’anjiang Model and the N-BEATS Model","authors":"Feng Ye,&nbsp;Zishuo Jin,&nbsp;Peng Zhang,&nbsp;Dong Xu,&nbsp;Lin Lan,&nbsp;Jian Sun","doi":"10.1155/int/8899669","DOIUrl":"https://doi.org/10.1155/int/8899669","url":null,"abstract":"<div>\u0000 <p>Digital twins are propelling the next generation of the industrial revolution and serve as a key technology in enabling intelligent water conservancy. However, due to the diversity of objects within water conservancy scenarios and the complexity of related factors, research and application of digital twins in the field of water conservancy remain immature. There are still significant challenges in constructing fine-grained, high-fidelity digital twin for water conservancy objects and their corresponding scenarios. In this context, taking polder areas as research subjects, a digital twin polder area system is proposed, which includes the data representation of the main elements in the polder area; based on 3D engine Unity, the modeling and rendering of the polder area’s terrain, water body, water conservancy projects, and different weather conditions are achieved; the Xin’anjiang model, N-BEATS model, and the feature engineering model we proposed are integrated to predict water level and flow rate, thereby driving the visual scenario to simulate the extent of the impact of waterlogging at different future moments. Based on satellite imagery data, actual water level data, and flow rate data from a polder area in the Lixiahe river network of Jiangsu Province in China, we measure the efficiency of scene rendering and the accuracy of the prediction models. The results show that the performance and model accuracy of the digital twin polder area system meet the practical requirements. It is more comprehensive by comparing with other works, which can be used as a reference for the construction of a digital twin system or scenario in the water conservancy field.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8899669","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112856","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":"Multiscale Features Integrated Model for Generalizable Deepfake Detection","authors":"Siqi Gu,&nbsp;Zihan Qin,&nbsp;Lizhe Xie,&nbsp;Zheng Wang,&nbsp;Yining Hu","doi":"10.1155/int/7084582","DOIUrl":"https://doi.org/10.1155/int/7084582","url":null,"abstract":"<div>\u0000 <p>Within the domain of Artificial Intelligence Generated Content (AIGC), technological strides in image generation have been marked, resulting in the proliferation of deepfake images that pose substantial security threats. The current landscape of deepfake detection technologies is marred by limited generalization across diverse generative models and a subpar detection rate for images generated through diffusion processes. In response to these challenges, this paper introduces a novel detection model designed for high generalizability, leveraging multiscale frequency and spatial domain features. Our model harnesses an array of specialized filters to extract frequency-domain characteristics, which are then integrated with spatial-domain features captured by a Feature Pyramid Network (FPN). The integration of the Attentional Feature Fusion (AFF) mechanism within the feature fusion module allows for the optimal utilization of the extracted features, thereby enhancing detection capabilities. We curated an extensive dataset encompassing deepfake images from a variety of GANs and diffusion models for rigorous evaluation. The experimental findings reveal that our proposed model achieves superior accuracy and generalization compared to existing baseline models when confronted with deepfake images from multiple generative sources. Notably, in cross-model detection scenarios, our model outperforms the next best model by a significant margin of 29.1% for diffusion-generated images and 15.1% for GAN-generated images. This accomplishment presents a viable solution to the pressing issues of generalization and adaptability in the field of deepfake detection.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7084582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112857","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":"Node Configuration Algorithm of Energy Heterogeneous Sensor Networks","authors":"Qian Sun,&nbsp;Xiangyue Meng,&nbsp;Xiao Peng,&nbsp;Zhiyao Zhao,&nbsp;Jiping Xu,&nbsp;Huiyan Zhang,&nbsp;Li Wang,&nbsp;Jiabin Yu,&nbsp;Xianglan Guo","doi":"10.1155/int/3949923","DOIUrl":"https://doi.org/10.1155/int/3949923","url":null,"abstract":"<div>\u0000 <p>The performance of heterogeneous sensor networks is enhanced by high-energy heterogeneous nodes. Determining the number and deployment of heterogeneous nodes is a significant research issue. A heterogeneous node configuration algorithm is presented in this paper, which can be used for overall network planning before the deployment of heterogeneous nodes. Subsequently, factors such as network performance and economic cost are comprehensively considered, and integrated into a single index using the entropy weighting method. The proportion of different indicators is then determined, and a formula for calculating the required number of heterogeneous nodes under various network conditions is derived by considering parameters such as network area size, node communication threshold distance, and the number of common nodes. Experimental results demonstrate that the proposed algorithm not only reduces networks costs but also enhances overall networks performance.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3949923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111469","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":"An ECA–ResNet-Based Intelligent Communication Scenario Identification Algorithm for 6G Wireless Communications","authors":"Wenqi Zhou,&nbsp;Cheng-Xiang Wang,&nbsp;Chen Huang,&nbsp;Rui Feng,&nbsp;Zhen Lv,&nbsp;Zhongyu Qian,&nbsp;Shuyi Ding","doi":"10.1155/int/8860822","DOIUrl":"https://doi.org/10.1155/int/8860822","url":null,"abstract":"<div>\u0000 <p>The sixth generation (6G) wireless communication envisions global coverage, all spectra, and full applications, which correspondingly creates many new communication scenarios. As the foundation of 6G communication system design, network planning, and optimization, more intelligent scenario identification algorithms are necessitated in wireless channel modeling to automatically match suitable parameters for various scenarios. With channel statistics and the efficient channel attention (ECA) mechanism, we propose an improved residual network (ResNet) to identify scenarios in the 6G space–air–ground–sea framework. Datasets from both channel measurements and 6G pervasive channel model (6GPCM) simulations are collected to establish a scenario channel characteristic database, including the numbered scenarios and channel statistical properties such as root mean square (RMS) delay spread (DS), RMS angle spread (AS), and stationary distance/time/bandwidth, etc. During the training and verification process, the proposed algorithm is optimized for 29 scenarios, and the identification accuracy of the proposed ECA–ResNet is higher than the convolutional neural network (CNN) and recurrent neural network (RNN). Finally, the cumulative distribution functions (CDFs) of RMS AS and RMS DS for interoffice main road, office outdoor, office, and industrial Internet of Things (IIoT) scenarios are verified according to the measurement data.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8860822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120863","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":"Nerve Segmentation of Ultrasound Images Bayesian U-Net Models","authors":"Taryn Michael,&nbsp;Ibidun Christiana Obagbuwa","doi":"10.1155/int/6114741","DOIUrl":"https://doi.org/10.1155/int/6114741","url":null,"abstract":"<div>\u0000 <p>Ultrasound imaging is a widely adopted method for noninvasive examination of internal structures, valued for its cost-effectiveness, real-time imaging capability, and absence of ionizing radiation. Its applications, including peripheral nerve blocking (PNB) procedures, benefit from the direct visualization of nerve structures. However, the inherent distortions in ultrasound images, arising from echo perturbations and speckle noise, pose challenges to the accurate localization of nerve structures, even for experienced practitioners. Computational techniques, particularly Bayesian inference, offer a promising solution by providing uncertainty estimates in model predictions. This article focused on developing and implementing an optimal Bayesian U-Net for nerve segmentation in ultrasound images, presented through a user-friendly application. Bayesian convolution layers and the Monte Carlo dropout method were the two Bayesian techniques explored and compared, with a specific emphasis on facilitating medical professionals’ decision-making processes. The research revealed that integrating the Monte Carlo dropout technique for Bayesian inference yields the most optimal results. The Bayesian model demonstrates an average binary accuracy of 98.99%, an average dice coefficient score of 0.72, and an average IOU score of 0.57 when benchmarked against a typical U-Net. The culmination of this work is an application designed for practical use by medical professionals, providing an intuitive interface for Bayesian nerve segmentation in ultrasound images. This research contributes to the broader understanding of Bayesian techniques in medical imaging models and offers a comprehensive solution that combines advanced methodology with user-friendly accessibility.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6114741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119812","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 Review on Reinforcement Learning for Motion Planning of Robotic Manipulators","authors":"Íñigo Elguea-Aguinaco,&nbsp;Ibai Inziarte-Hidalgo,&nbsp;Simon Bøgh,&nbsp;Nestor Arana-Arexolaleiba","doi":"10.1155/int/1636497","DOIUrl":"https://doi.org/10.1155/int/1636497","url":null,"abstract":"<div>\u0000 <p>Effective motion planning is an indispensable prerequisite for the optimal performance of robotic manipulators in any task. In this regard, the research and application of reinforcement learning in robotic manipulators for motion planning have gained great relevance in recent years. The ability of reinforcement learning agents to adapt to variable environments, especially those featuring dynamic obstacles, has propelled their increasing application in this domain. Notwithstanding, a clear need remains for a resource that critically examines the progress, challenges, and future directions of this machine learning control technique in motion planning. This article undertakes a comprehensive review of the landscape of reinforcement learning, offering a retrospective analysis of its application in motion planning from 2018 to the present. The exploration extends to the trends associated with reinforcement learning in the context of serial manipulators and motion planning, as well as the various technological challenges currently presented by this machine learning control technique. The overarching objective of this review is to serve as a valuable resource for the robotics community, facilitating the ongoing development of systems controlled by reinforcement learning. By delving into the primary challenges intrinsic to this technology, the review seeks to enhance the understanding of reinforcement learning’s role in motion planning and provides insights that may suggest future research directions in this domain.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1636497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118897","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":"Digital Twin-Enabled Delay Diagnosis Traceability and Propagation Process for Airport Flight Ground Service","authors":"Chang Liu,&nbsp;YuanYuan Zhang,&nbsp;YanRu Chen,&nbsp;ShiJia Liu,&nbsp;ShunFang Hu,&nbsp;Qian Luo,&nbsp;LiangYin Chen","doi":"10.1155/int/7458758","DOIUrl":"https://doi.org/10.1155/int/7458758","url":null,"abstract":"<div>\u0000 <p>The emergence of digital twin technology offers a promising solution to address the limitations of traditional methods on early diagnosis and accurate propagation analysis of flight ground service delays. However, the application of digital twin technology in the civil aviation domain still stays at the lower maturity of the L2 level, which focuses on physical assets, operational data, and maintenance planning at airports, and failed to achieve the integration of flight ground operation mechanism and real-time data, making it difficult to realize timely delay diagnosis. The simulation model is also limited to the offline simulation technology, which cannot connect to real-time data for simulation from intermediate processes. In this work, we developed an advanced L3-level airport digital twin system for flight ground service processes delay diagnosis and propagation, which focused on real-time data-driven simulation models and machine learning applications to meet the timely and precision requirements. First, we used the Unity3D platform to construct static three-dimensional models of flight ground service objects on the airport cloud server. By parsing these behavioral state interfaces and mapping real-time dynamic data from the airport sensing and business systems, we achieved accurate visualization of the airport’s dynamic operational processes. Then, a vehicle delay tree–based Bayesian diagnostic model was proposed in the digital twin system to analyze the relationships between multiple flights and service processes, which enables proactive diagnosis of the operation status and provides delay warning information. To improve the accuracy of propagation analysis, we proposed a “breakpoint” simulation method that enables real-time simulation starting from an intermediate moment, facilitating the inference of flight ground service delays since the early warning moment. In addition, two delay tracing and propagation algorithms were proposed to identify delays and investigate propagation paths. Leveraging real-time operational information, our approach provides valuable feedback for decision-making, empowering the airport manager to formulate precise optimization strategies. Experiments on real-world airport data have validated the effectiveness of our proposed method and provided practical recommendations for airport managers to reduce aircraft delays and improve airport operation efficiency.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7458758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118898","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":"Mind to Music: An EEG Signal-Driven Real-Time Emotional Music Generation System","authors":"Shuang Ran,&nbsp;Wei Zhong,&nbsp;Lin Ma,&nbsp;Danting Duan,&nbsp;Long Ye,&nbsp;Qin Zhang","doi":"10.1155/int/9618884","DOIUrl":"https://doi.org/10.1155/int/9618884","url":null,"abstract":"<div>\u0000 <p>Music is an important way for emotion expression, and traditional manual composition requires a solid knowledge of music theory. It is needed to find a simple but accurate method to express personal emotions in music creation. In this paper, we propose and implement an EEG signal-driven real-time emotional music generation system for generating exclusive emotional music. To achieve real-time emotion recognition, the proposed system can obtain the model suitable for a newcomer quickly through short-time calibration. And then, both the recognized emotion state and music structure features are fed into the network as the conditional inputs to generate exclusive music which is consistent with the user’s real emotional expression. In the real-time emotion recognition module, we propose an optimized style transfer mapping algorithm based on simplified parameter optimization and introduce the strategy of instance selection into the proposed method. The module can obtain and calibrate a suitable model for a new user in short-time, which achieves the purpose of real-time emotion recognition. The accuracies have been improved to 86.78% and 77.68%, and the computing time is just to 7 s and 10 s on the public SEED and self-collected datasets, respectively. In the music generation module, we propose an emotional music generation network based on structure features and embed it into our system, which breaks the limitation of the existing systems by calling third-party software and realizes the controllability of the consistency of generated music with the actual one in emotional expression. The experimental results show that the proposed system can generate fluent, complete, and exclusive music consistent with the user’s real-time emotion recognition results.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9618884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118107","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}