Cognitive Computation最新文献

{"title":"SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems","authors":"Mahmuda Akter, Nour Moustafa, Benjamin Turnbull","doi":"10.1007/s12559-024-10310-3","DOIUrl":"https://doi.org/10.1007/s12559-024-10310-3","url":null,"abstract":"<p>Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"86 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score","authors":"Zhirui Zeng, Jialing He, Tao Xiang, Ning Wang, Biwen Chen, Shangwei Guo","doi":"10.1007/s12559-024-10315-y","DOIUrl":"https://doi.org/10.1007/s12559-024-10315-y","url":null,"abstract":"<p>The burgeoning practice of unauthorized acquisition and utilization of personal textual data (e.g., social media comments and search histories) by certain entities has become a discernible trend. To uphold data protection regulations such as the Asia–Pacific Privacy Initiative (APPI) and to identify instances of unpermitted exploitation of personal data, we propose a novel and efficient audit framework that helps users conduct cognitive analysis to determine if their textual data was used for data augmentation. and training a discriminative model. In particular, we focus on auditing models that use BERT as the backbone for discriminating text and are at the core of popular online services. We first propose an accumulated discrepancy score, which involves not only the response of the target model to the auditing sample but also the responses between pre-trained and finetuned models, to identify membership. We implement two types of audit methods (i.e., sample-level and user-level) according to our framework and conduct comprehensive experiments on two downstream applications to evaluate the performance. The experimental results demonstrate that our sample-level auditing achieves an AUC of 89.7% and an accuracy of 83%, whereas the user-level method can audit membership with an AUC of 89.7% and an accuracy of 88%. Additionally, we undertake an analysis of how augmentation methods impact auditing performance and expound upon the underlying reasons for these observations.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects","authors":"Abiy Abinet Mamo, Bealu Girma Gebresilassie, Aniruddha Mukherjee, Vikas Hassija, Vinay Chamola","doi":"10.1007/s12559-024-10291-3","DOIUrl":"https://doi.org/10.1007/s12559-024-10291-3","url":null,"abstract":"<p>In medical imaging, traditional methods have long been relied upon. However, the integration of Generative Adversarial Networks (GANs) has sparked a paradigm shift, ushering in a new era of innovation. Our comprehensive investigation explores the groundbreaking impact of GANs on medical imaging, examining the evolution from traditional techniques to GAN-driven approaches. Through meticulous analysis, we dissect various aspects of GANs, encompassing their taxonomy, historical progression, and diverse iterations such as Self-Attention GANs (SAGAN), Conditional GANs, and Progressive Growing GANs (PGGAN). Complemented by a practical case study, we scrutinize the extensive applications of GANs, spanning image generation, reconstruction, enhancement, segmentation, and super-resolution. Despite promising prospects, enduring challenges including data scarcity, interpretability issues, and ethical concerns persist. Looking ahead, we anticipate advancements in personalized and pathological image generation, cross-modal synthesis, real-time interactive image generation, and enhanced anomaly detection. Through this review, we underscore the transformative potential of GANs in reshaping medical imaging practices, while also outlining avenues for future research endeavors.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"112 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"CPD-NSL: A Two-Stage Brain Effective Connectivity Network Construction Method Based on Dynamic Bayesian Network","authors":"Zhiqiong Wang, Qi Chen, Zhongyang Wang, Xinlei Wang, Luxuan Qu, Junchang Xin","doi":"10.1007/s12559-024-10296-y","DOIUrl":"https://doi.org/10.1007/s12559-024-10296-y","url":null,"abstract":"<p>Current brain science reveals that the connectivity patterns of the human brain are constantly changing when performing different tasks. Thus, brain effective connectivity networks based on non-stationary assumption can describe such neurodynamics better than the ones based on stationary assumption. However, existing methods for inferring non-stationary brain effective connectivity networks are committed to estimating the change points and network structures simultaneously. It is even worse that these methods will inevitably focus on one part of the estimation process and lead to the deviation of the results obtained by the other part. Then, the construction results of non-stationary brain effective connectivity networks cannot accurately reflect the real brain dynamics. In this paper, a novel approach to constructing non-stationary brain effective connectivity networks is proposed, namely CPD-NSL. It involves two stages including change point detection and network structure learning. In the first stage, the latent block model is used, and then the improved forward-backward search method is used to construct the stationary networks between adjacent change points in the network structure learning part. Finally, the constructed stationary networks are arranged in chronological order to obtain the final time-varying brain effective connectivity network. CPD-NSL is validated using simulated data as well as real fMRI data from HCP public datasets. The results show that CPD-NSL can restore the real network more accurately and consume less time. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method in constructing non-stationary state brain effective connectivity networks.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"160 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Effect of Leakage Delays on Bifurcation in Fractional-Order Bidirectional Associative Memory Neural Networks with Five Neurons and Discrete Delays","authors":"Yangling Wang, Jinde Cao, Chengdai Huang","doi":"10.1007/s12559-024-10305-0","DOIUrl":"https://doi.org/10.1007/s12559-024-10305-0","url":null,"abstract":"<p>As is well known that time delays are inevitable in practice due to the finite switching speed of amplifiers and information transmission between neurons. So the study on the Hopf bifurcation of delayed neural networks has aroused extensive attention in recent years. However, it’s worth mentioning that only the communication delays between neurons were generally considered in most existing relevant literatures. Actually, it has been proven that a kind of so-called leakage delays cannot be ignored because the self-decay process of a neuron’s action potential is not instantaneous in hardware implementation of neural networks. Though leakage delays have been taken into account in a few more recent works concerning the Hopf bifurcation of fractional-order bidirectional associative memory neural networks, the addressed neural networks were low-dimension or the involved time delays were single. In this paper, we propose a five-neuron fractional-order bidirectional associative memory neural network model, which includes leakage delays and discrete communication delays to meet the characteristics of real neural networks better. Then we use the stability theory of fractional differential equations and Hopf bifurcation theory to investigate its dynamic behavior of Hopf bifurcation. The Hopf bifurcation of the proposed model are studied by taking the involved two different leakage delays as the bifurcation parameter respectively, and two kinds of sufficient conditions for Hopf bifurcation are obtained. A numerical example as well as its simulation plots and phase portraits are given at last. Our results indicate that a Hopf bifurcation rises near the zero equilibrium point when the leakage delay reaches its critical value which is given by an explicit formula. Particularly, the results of numerical simulations show that the leakage delay would narrow the stability region of the proposed system and make the Hopf bifurcation occur earlier.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"311 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141257565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Optimization Based Deep Learning for COVID-19 Detection Using Respiratory Sound Signals","authors":"Jawad Ahmad Dar, Kamal Kr Srivastava, Sajaad Ahmed Lone","doi":"10.1007/s12559-024-10300-5","DOIUrl":"https://doi.org/10.1007/s12559-024-10300-5","url":null,"abstract":"&lt;p&gt;The COVID-19 prediction process is more indispensable to handle the spread and death occurred rate because of COVID-19. However, early and precise prediction of COVID-19 is more difficult, because of different sizes and resolutions of input image. Thus, these challenges and problems experienced by traditional COVID-19 detection methods are considered as major motivation to develop SJHBO-based Deep Q Network. The classification issue of respiratory sound has perceived a great focus from the clinical scientists as well as the community of medical researcher in the previous year for the identification of COVID-19 disease. The major contribution of this research is to design an effectual COVID-19 detection model using devised SJHBO-based Deep Q Network. In this paper, the COVID-19 detection is carried out by the deep learning with optimization technique, namely Snake Jaya Honey Badger Optimization (SJHBO) algorithm-driven Deep Q Network. Here, the SJHBO algorithm is the incorporation of Jaya Honey Badger Optimization (JHBO) along with Snake optimization (SO). Here, the COVID-19 is detected by the Deep Q Network wherein the weights of Deep Q Network are tuned by the SJHBO algorithm. Moreover, JHBO is modelled by hybrids, which are the Jaya algorithm and Honey Badger Optimization (HBO) algorithm. Furthermore, the features, such as spectral contrast, Mel frequency cepstral coefficients (MFCC), empirical mode decomposition (EMD) algorithm, spectral flux, fast Fourier transform (FFT), spectral roll-off, spectral centroid, zero-crossing rate, root mean square energy, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude, are mined for enlightening the detection performance. The developed method realized the better performance based on the accuracy, sensitivity and specificity of 0.9511, 0.9506 and 0.9469. All test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. Statistical analysis is performed to analyze the performance of the proposed method based on testing accuracy, sensitivity and specificity. Hence, this paper presents the newly devised SJHBO-based Deep Q-Net for COVID-19 detection. This research considers the audio samples as an input, which is acquired from the Coswara dataset. The SJHBO-based Deep Q network approach is developed for COVID-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features that can be extracted for further improving the detection performance. The proposed COVID-19 detection method is useful in various applications, like medical and so on. Developed SJHBO-enabled Deep Q network for COVID-19 detection: An effective COVID-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The Deep Q Network is used for detecting COVID-19, which classifies the feature vector ","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Intelligent Fisheries: Cognitive Solutions for Improving Aquaculture Commercial Efficiency Through Enhanced Biomass Estimation and Early Disease Detection","authors":"Kanwal Aftab, Linda Tschirren, Boris Pasini, Peter Zeller, Bostan Khan, Muhammad Moazam Fraz","doi":"10.1007/s12559-024-10292-2","DOIUrl":"https://doi.org/10.1007/s12559-024-10292-2","url":null,"abstract":"<p>With the burgeoning global demand for seafood, potential solutions like aquaculture are increasingly significant, provided they address issues like pollution and food security challenges in a sustainable manner. However, significant obstacles such as disease outbreaks and inaccurate biomass estimation underscore the need for optimized solutions. This paper proposes “Fish-Sense”, a deep learning-based pipeline inspired by the human visual system’s ability to recognize and classify objects, developed in conjunction with fish farms, aiming to enhance disease detection and biomass estimation in the aquaculture industry. Our automated framework is two-pronged: one module for biomass estimation using deep learning algorithms to segment fish, classify species, and estimate biomass; and another for disease symptom detection symptoms, employing deep learning algorithms to classify fish into healthy and unhealthy categories, and subsequently identifying symptoms and locations of bacterial infections if a fish is classified as unhealthy. To overcome data scarcity in this field, we have created four novel real-world datasets for fish segmentation, health classification, species classification, and fish part segmentation. Our biomass estimation algorithms demonstrated substantial accuracy across five species, and the health classification. These algorithms provide a foundation for the development of industrial software solutions to improve fish health monitoring in aquaculture farms. Our integrated pipeline facilitates the transition from research to real-world applications, potentially encouraging responsible aquaculture practices. Nevertheless, these advancements must be seen as part of a comprehensive strategy aimed at improving the aquaculture industry’s sustainability and efficiency, in line with the United Nations’ Sustainable Development Goals’ evolving interpretations. The code, trained models, and the data for this project can be obtained from the following GitHub repository: https://github.com/Vision-At-SEECS/Fish-Sense.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"17 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Review of Key Technologies for Emotion Analysis Using Multimodal Information","authors":"Xianxun Zhu, Chaopeng Guo, Heyang Feng, Yao Huang, Yichen Feng, Xiangyang Wang, Rui Wang","doi":"10.1007/s12559-024-10287-z","DOIUrl":"https://doi.org/10.1007/s12559-024-10287-z","url":null,"abstract":"<p>Emotion analysis, an integral aspect of human–machine interactions, has witnessed significant advancements in recent years. With the rise of multimodal data sources such as speech, text, and images, there is a profound need for a comprehensive review of pivotal elements within this domain. Our paper delves deep into the realm of emotion analysis, examining multimodal data sources encompassing speech, text, images, and physiological signals. We provide a curated overview of relevant literature, academic forums, and competitions. Emphasis is laid on dissecting unimodal processing methods, including preprocessing, feature extraction, and tools across speech, text, images, and physiological signals. We further discuss the nuances of multimodal data fusion techniques, spotlighting early, late, model, and hybrid fusion strategies. Key findings indicate the essentiality of analyzing emotions across multiple modalities. Detailed discussions on emotion elicitation, expression, and representation models are presented. Moreover, we uncover challenges such as dataset creation, modality synchronization, model efficiency, limited data scenarios, cross-domain applicability, and the handling of missing modalities. Practical solutions and suggestions are provided to address these challenges. The realm of multimodal emotion analysis is vast, with numerous applications ranging from driver sentiment detection to medical evaluations. Our comprehensive review serves as a valuable resource for both scholars and industry professionals. It not only sheds light on the current state of research but also highlights potential directions for future innovations. The insights garnered from this paper are expected to pave the way for subsequent advancements in deep multimodal emotion analysis tailored for real-world deployments.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"95 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"RA-Net: Region-Aware Attention Network for Skin Lesion Segmentation","authors":"Asim Naveed, Syed S. Naqvi, Shahzaib Iqbal, Imran Razzak, Haroon Ahmed Khan, Tariq M. Khan","doi":"10.1007/s12559-024-10304-1","DOIUrl":"https://doi.org/10.1007/s12559-024-10304-1","url":null,"abstract":"<p>The precise segmentation of skin lesion in dermoscopic images is essential for the early detection of skin cancer. However, the irregular shapes of the lesions, the absence of sharp edges, the existence of artifacts like hair follicles, and marker color make this task difficult. Currently, fully connected networks (FCNs) and U-Nets are the most commonly used techniques for melanoma segmentation. However, as the depth of these neural network models increases, they become prone to various challenges. The most pertinent of these challenges are the vanishing gradient problem and the parameter redundancy problem. These can result in a decline in Jaccard index of the segmentation model. This study introduces a novel end-to-end trainable network designed for skin lesion segmentation. The proposed methodology consists of an encoder-decoder, a region-aware attention approach, and guided loss function. The trainable parameters are reduced using depth-wise separable convolution, and the attention features are refined using a guided loss, resulting in a high Jaccard index. We assessed the effectiveness of our proposed RA-Net on four frequently utilized benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The empirical results validate that our method achieves state-of-the-art performance, as indicated by a notably high Jaccard index.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Enhanced Android Ransomware Detection Through Hybrid Simultaneous Swarm-Based Optimization","authors":"Moutaz Alazab, Ruba Abu Khurma, David Camacho, Alejandro Martín","doi":"10.1007/s12559-024-10301-4","DOIUrl":"https://doi.org/10.1007/s12559-024-10301-4","url":null,"abstract":"<p>Ransomware is a significant security threat that poses a serious risk to the security of smartphones, and its impact on portable devices has been extensively discussed in a number of research papers. In recent times, this threat has witnessed a significant increase, causing substantial losses for both individuals and organizations. The emergence and widespread occurrence of diverse forms of ransomware present a significant impediment to the pursuit of reliable security measures that can effectively combat them. This constitutes a formidable challenge due to the dynamic nature of ransomware, which renders traditional security protocols inadequate, as they might have a high false alarm rate and exert significant processing demands on mobile devices that are restricted by limited battery life, CPU, and memory. This paper proposes a novel intelligent method for detecting ransomware that is based on a hybrid multi-solution binary JAYA algorithm with a single-solution simulated annealing (SA). The primary objective is to leverage the exploitation power of SA in supporting the exploration power of the binary JAYA algorithm. This approach results in a better balance between global and local search milestones. The empirical results of our research demonstrate the superiority of the proposed SMO-BJAYA-SA-SVM method over other algorithms based on the evaluation measures used. The proposed method achieved an accuracy rate of 98.7%, a precision of 98.6%, a recall of 98.7%, and an F1 score of 98.6%. Therefore, we believe that our approach is an effective method for detecting ransomware on portable devices. It has the potential to provide a more reliable and efficient solution to this growing security threat.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"26 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}