International Journal of Intelligent Systems最新文献

{"title":"An Improved Particle Swarm Optimization Method for Nonlinear Optimization","authors":"Shiwei Liu,&nbsp;Xia Hua,&nbsp;Longxiang Shan,&nbsp;Dongqiao Wang,&nbsp;Yong Liu,&nbsp;Qiaohua Wang,&nbsp;Yanhua Sun,&nbsp;Lingsong He","doi":"10.1155/2024/6628110","DOIUrl":"https://doi.org/10.1155/2024/6628110","url":null,"abstract":"<div>\u0000 <p>Nonlinear optimization is becoming more challenging in information sciences and various industrial applications, but nonlinear problems solved by the classical particle swarm-based methods are usually characterized by low efficiency, accuracy, and convergence speed in specific issues. To solve these problems and enhance the nonlinear optimization performance, an improved metaheuristic particle swarm optimization (PSO) model is proposed here. First, the optimization principles and model of the new method are introduced, and algorithms of the improved PSO are presented by updating the displacement and velocity of the moving particle according to Euler–Maruyama (EM) principle rather than traditional standard normal distribution. Then, the influence of the model parameters, input dimensions, and different nonlinear problems on the PSO optimization characterizations are studied by Pareto set solving and optimization performance comparison. The analysis regarding diverse nonlinear problems and optimization methods manifests that the improved method is capable of solving various nonlinear problems especially for multiobjective models, while the robustness and reliability can always keep consistent regardless of the change of model parameters. Finally, the performance evaluation is exhibited by the case study of nonlinear parameter optimization, 3 groups of CEC benchmark problems, and rank-sum test for 6 comparable optimization algorithms, which all verify its effectiveness and reliability, as well as the significance and great application promise. The results show that the new proposed PSO method has the fastest convergence speed and least iteration numbers in searching for the global best solution of 9 nonlinear problems among 8 different optimization models indicated by the <i>p</i> values smaller than 0.05. Additionally, the main conclusions showing the calculation efficiency, stability, robustness, and great application promise of the proposed method are summarized, and future work is discussed.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6628110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525235","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":"LDHD-Net: A Lightweight Network With Double Branch Head for Feature Enhancement of UAV Targets in Complex Scenes","authors":"Cong Zhang,&nbsp;Qi Gao,&nbsp;Rui Shi,&nbsp;Mingkai Yue","doi":"10.1155/2024/7259029","DOIUrl":"https://doi.org/10.1155/2024/7259029","url":null,"abstract":"<div>\u0000 <p>The development of small UAV technology has led to the emergence of new challenges in UAV countermeasures. The timely detection of UAVs can effectively prevent potential infringements on airspace and privacy. Currently, methods based on deep learning demonstrate excellent performance in target detection. However, in complex scenes, there is a tendency for false alarms (FAs) and misdetections to occur at a higher rate. To solve these problems, we propose a lightweight infrared small target detection algorithm LDHD-Net. First, we design a novel Ghost-Shuffle module in the backbone network to enhance the network feature extraction capability. Meanwhile, we remove redundant layers from the network to make the backbone network more lightweight. Second, we design a hierarchical attention enhancement module in the neck network to improve the saliency of UAV targets and reduce background noise interference. In addition, we design a novel small target detection structure and prediction heads in the shallow layers of the network to improve small target detection accuracy. Finally, we design a novel attention dual-branch head to reduce interference between different tasks and improve the real-time performance of algorithm detection. The experimental results show that compared with the original model, inference time remains essentially the same, LDHD-Net parameters are only 3.9 M and AP improves by 12.6%. Compared to SOTA methods, LDHD-Net shows better performance on SIDD and Anti-UAV410 datasets. The algorithm effectively improves the accuracy and real-time detection of UAVs in complex scenes.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7259029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525233","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":"ESTS-GCN: An Ensemble Spatial–Temporal Skeleton-Based Graph Convolutional Networks for Violence Detection","authors":"Nourah Fahad Janbi,&nbsp;Musrea Abdo Ghaseb,&nbsp;Abdulwahab Ali Almazroi","doi":"10.1155/2024/2323337","DOIUrl":"https://doi.org/10.1155/2024/2323337","url":null,"abstract":"<div>\u0000 <p>Surveillance systems are essential for social and personal security. However, monitoring multiple video feeds with multiple targets is challenging for human operators. Therefore, automatic and smart surveillance systems have been introduced to support or replace traditional surveillance systems and build safer communities. Advancements in artificial intelligence techniques, particularly in the field of computer vision, have boosted this area of research. Most existing works have focused on image-based (RGB-based) machine learning and deep learning algorithms for detecting anomalous and violent events. In this study, we propose a unique Ensemble Spatial–Temporal Skeleton-Based Graph Convolutional Networks (ESTS-GCNs) model for violence detection that automatically uses spatial and temporal data to detect violence in surveillance videos. Skeleton-based algorithms are less sensitive to pixel-based noise and background interference, making them excellent candidates for activity and anomaly detection. Our proposed ensemble-based architecture utilizes Graph Convolutional Networks (GCNs) and comprises multiple spatial and temporal modules. Three different spatial pipelines are exploited: channel-wise topologies, self-attention mechanism, and graph attention networks. The models were trained and evaluated using two skeleton-based datasets introduced by us: Skeleton-based Real-Life Violence Situations (RLVS) and NTU-Violence (NTU-V). Our model achieved a maximum accuracy of around 93% and outperformed existing models by more than 10%.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2323337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525234","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":"Some Information Measures for Interval-Valued Hesitant Fuzzy Sets in Multiple Criteria Decision-Making","authors":"Kun Chen,&nbsp;Jiyu Tan,&nbsp;Chuanxi Zhu,&nbsp;Gaochang Liu","doi":"10.1155/2024/6186183","DOIUrl":"https://doi.org/10.1155/2024/6186183","url":null,"abstract":"<div>\u0000 <p>In the fields of information science and artificial intelligence, dealing with uncertainty, fuzziness, and complexity has always been a hot and difficult research topic. Especially in modern society, with the continuous development of science and technology, people are facing more and more complex problems. Interval-valued hesitant fuzzy set (IVHFS) is an extended form of a fuzzy set. It can more flexibly express and handle uncertainty and fuzziness in decision-making processes. However, in the practical application of the IVHFS, its information measurement is crucial, which is directly related to the application value of the IVHFS in various fields. Therefore, studying the information measurement of the IVHFS has important theoretical significance and practical value for the fields of information science and artificial intelligence. In spite of significant advances, entropy and similarity as the well-known information measures for interval-valued hesitant fuzzy information have not yet been thoroughly researched. In this contribution, we investigate information measures in the IVHFS, including nonprobabilistic entropy, similarity, and cross-entropy. We first analyze the change law of hesitating uncertainty and fuzzy uncertainty in geometric space, and a nonprobabilistic entropy measurement method and its axiomatic definition for IVHFS are further developed. Then, a novel similarity measurement formula for IVHFS and its axiomatic requirements are proposed on the basis of the two nonfuzzy elements (<span></span><math></math> and <span></span><math></math>). Furthermore, the novel similarity measure is used to construct the cross-entropy measure for IVHFS and its axiomatic requirements based on the association between the similarity and the cross-entropy. Lastly, a MAGDM method is proposed by using the developed three information measures, and the efficacy of the proposed method is demonstrated by a numerical example of emergency communication support capacity evaluation. Comparative analysis and computational cost analysis are implemented to demonstrate the superiority and validity of the proposed information measures.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6186183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525281","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":"Optimization of Biped Robot Walking Based on the Improved Particle Swarm Algorithm","authors":"Chao Zhang,&nbsp;Mei Liu,&nbsp;Peisi Zhong,&nbsp;Shihao Yang,&nbsp;Zhongyuan Liang,&nbsp;Qingjun Song","doi":"10.1155/2024/6689071","DOIUrl":"https://doi.org/10.1155/2024/6689071","url":null,"abstract":"<div>\u0000 <p>The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm’s exploration and exploitation capabilities. The BPSO algorithm’s solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO-based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG-controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6689071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525013","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":"Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach","authors":"Dilawar Shah,&nbsp;Mohammad Asmat Ullah Khan,&nbsp;Mohammad Abrar,&nbsp;Muhammad Tahir","doi":"10.1155/2024/5564649","DOIUrl":"https://doi.org/10.1155/2024/5564649","url":null,"abstract":"<div>\u0000 <p>In the global fight against breast cancer, the importance of early diagnosis is unparalleled. Early identification not only improves treatment options but also significantly improves survival rates. Our research introduces an innovative ensemble method that synergistically combines the strengths of four state-of-the-art convolutional neural networks (CNNs): EfficientNet, AlexNet, ResNet, and DenseNet. These networks were chosen for their architectural advances and proven efficacy in image classification tasks, particularly in medical imaging. Each network within our ensemble is uniquely optimized: EfficientNet is fine-tuned with customized scaling to address dataset specifics; AlexNet employs a variable dropout mechanism to reduce overfitting; ResNet benefits from learnable weighted skip connections for better gradient flow; and DenseNet uses selective connectivity to balance computational efficiency and feature extraction. This ensembling strategy combines the predictive output of multiple CNNs, each trained with an individually optimized network, to enhance the ensemble’s overall diagnostic performance. This provides higher precision and stability than any model and shows outstanding performance in the early stage of breast cancer with a precision of up to 94.6%, sensitivity of 92.4%, specificity of 96.1%, and area under the curve (AUC) of 98.0%. This ensemble framework indicated a leap in the early diagnosis of breast cancer as it is a powerful tool that combines several state-of-the-art techniques, hence providing better results.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5564649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524707","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":"EWRD: Entropy-Weighted Low-Light Image Enhancement via Reverse Diffusion Model","authors":"Yuheng Wu,&nbsp;Guangyuan Wu,&nbsp;Ronghao Liao","doi":"10.1155/2024/4650233","DOIUrl":"https://doi.org/10.1155/2024/4650233","url":null,"abstract":"<div>\u0000 <p>Low-light enhancement significantly aids vision tasks under poor illumination conditions. Existing methods primarily focus on enhancing severely degraded low-light areas, improving illumination accuracy, or noise suppression, yet they often overlook the loss of additional details and color distortion during calculation. In this paper, we propose an innovative entropy-weighted low-light image enhancement method via the reverse diffusion model, aiming at addressing the limitations of the traditional Retinex decomposition model in preserving local pixel details and handling excessive smoothing issues. This method integrates an entropy-weighting mechanism for improved image quality and entropy, along with a reverse diffusion model to address the detail loss in total variation regularization and refine the enhancement process. Furthermore, we utilize long short-term memory networks for the learning reverse process and the simulation of image degradation, based on a thermodynamics-based nonlinear anisotropic diffusion model. Comparative experiments reveal the superiority of our method over conventional Retinex-based approaches in terms of detail preservation and visual quality. Extensive tests across diverse datasets demonstrate the exceptional performance of our method, evidencing its potential as a robust solution for low-light image enhancement.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4650233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524894","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":"E-Speech: Development of a Dataset for Speech Emotion Recognition and Analysis","authors":"Wenjin Liu,&nbsp;Jiaqi Shi,&nbsp;Shudong Zhang,&nbsp;Lijuan Zhou,&nbsp;Haoming Liu","doi":"10.1155/2024/5410080","DOIUrl":"https://doi.org/10.1155/2024/5410080","url":null,"abstract":"<div>\u0000 <p>Speech emotion recognition plays a crucial role in analyzing psychological disorders, behavioral decision-making, and human-machine interaction applications. However, the majority of current methods for speech emotion recognition heavily rely on data-driven approaches, and the scarcity of emotion speech datasets limits the progress in research and development of emotion analysis and recognition. To address this issue, this study introduces a new English speech dataset specifically designed for emotion analysis and recognition. This dataset consists of 5503 voices from over 60 English speakers in different emotional states. Furthermore, to enhance emotion analysis and recognition, fast Fourier transform (FFT), short-time Fourier transform (STFT), mel-frequency cepstral coefficients (MFCCs), and continuous wavelet transform (CWT) are employed for feature extraction from the speech data. Utilizing these algorithms, the spectrum images of the speeches are obtained, forming four datasets consisting of different speech feature images. Furthermore, to evaluate the dataset, 16 classification models and 19 detection algorithms are selected. The experimental results demonstrate that the majority of classification and detection models achieve exceptionally high recognition accuracy on this dataset, confirming its effectiveness and utility. The dataset proves to be valuable in advancing research and development in the field of emotion recognition.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5410080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524705","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":"Collaborative Attack Sequence Generation Model Based on Multiagent Reinforcement Learning for Intelligent Traffic Signal System","authors":"Yalun Wu,&nbsp;Yingxiao Xiang,&nbsp;Thar Baker,&nbsp;Endong Tong,&nbsp;Ye Zhu,&nbsp;Xiaoshu Cui,&nbsp;Zhenguo Zhang,&nbsp;Zhen Han,&nbsp;Jiqiang Liu,&nbsp;Wenjia Niu","doi":"10.1155/2024/4734030","DOIUrl":"https://doi.org/10.1155/2024/4734030","url":null,"abstract":"<div>\u0000 <p>Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi-Actor-Attention-Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single-vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4734030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524703","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":"Fast Subpixel Motion Estimation Based on Human Visual System","authors":"Dadvar Hosseini Avashanagh,&nbsp;Mehdi Nooshyar,&nbsp;Saeed Barghandan,&nbsp;Majid Ghandchi","doi":"10.1155/2024/6168548","DOIUrl":"https://doi.org/10.1155/2024/6168548","url":null,"abstract":"<div>\u0000 <p>More than 80% of video coding times are consumed by motion estimation calculations, which are the most complex aspect of the process. This method eliminates temporal redundancies in a video sequence to achieve maximum compression. Numerous efforts have been made to bring calculations closer to real time, yielding fruitful results. This study proposes a fast subpixel motion estimation algorithm for video encoding with fewer search points. This method employs the capabilities of human visual systems (HVSs), physical motion characteristics of real-world objects, and special image information from successive frames. The number of search points (NSP) using the statistical data of the movement of the blocks in the frames of video sequences is reduced to apply fewer calculations to the system while maintaining the quality of images. Therefore, it is possible to approach fast and real-time calculations instead of time-consuming algorithms by accurately modeling this algorithm.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6168548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524704","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}