PeerJ Computer Science最新文献

{"title":"Linear B-cell epitope prediction for SARS and COVID-19 vaccine design: Integrating balanced ensemble learning models and resampling strategies.","authors":"Fatih Gurcan","doi":"10.7717/peerj-cs.2970","DOIUrl":"10.7717/peerj-cs.2970","url":null,"abstract":"<p><p>This study presents a comprehensive machine learning framework to enhance the prediction accuracy of B-cell epitopes and antibody recognition related to Severe Acute Respiratory Syndrome (SARS) and Coronavirus Disease 2019 (COVID-19). To address the issue of data imbalance, various resampling techniques were applied using three types of strategies: over-sampling, under-sampling, and hybrid-sampling. The implemented resampling methods were designed to improve class balance and enhance model training. The rebalanced datasets were then used in model building with ensemble classifiers employing Boosting, Bagging, and Balancing strategies. Hyperparameter optimization for the classifiers was conducted using GridSearchCV, while feature selection was performed with the recursive feature elimination (RFE) algorithm. Model performance was evaluated using seven different metrics: Accuracy, Precision, Recall, F1-score, receiver operating characteristic area under the curve (ROC AUC), precision recall area under the curve (PR AUC), and Matthews correlation coefficient (MCC). Furthermore, statistical significance analyses including paired t-test, Wilcoxon, and permutation tests confirmed the reliability of the model improvements. To interpret the model's predictive behavior, peptides with the highest confidence among correctly classified instances were identified as potential epitope candidates. The results indicate that the combination of Synthetic Minority Over-Sampling Technique-Edited Nearest Neighbors (SMOTE-ENN), and ExtraTrees yielded the best performance, achieving an ROC AUC score of 0.9899. The combination of Instance Hardness Threshold (IHT) and ExtraTrees followed closely with a score of 0.9799. These findings emphasize the effectiveness of integrating resampling models and balancing ensemble classifiers in improving the accuracy of B-cell epitope prediction and antibody recognition for SARS and COVID-19 infections. This study contributes to vaccine development efforts and the advancement of immunoinformatics research by identifying promising epitope candidates.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2970"},"PeriodicalIF":3.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Fusing Transformer-XL with bi-directional recurrent networks for cyberbullying detection.","authors":"Md Mithun Hossain, Md Shakil Hossain, Md Shakhawat Hossain, M Firoz Mridha, Mejdl Safran, Sultan Alfarhood, Dunren Che","doi":"10.7717/peerj-cs.2940","DOIUrl":"10.7717/peerj-cs.2940","url":null,"abstract":"<p><p>Identifying cyberbullying in languages other than English presents distinct difficulties owing to linguistic subtleties and scarcity of annotated datasets. This article presents a new method for identifying cyberbullying in Bengali text data using the Kaggle dataset. This strategy combines Transformer-Extra Large (XL) with bi-directional recurrent neural networks (BiGRU-BiLSTM). Extensive data preparation was performed, including data cleaning, data analysis, and label encoding. Upsampling methods were used to handle imbalanced classes, and data augmentation enhanced the training dataset. We carried out tokenization of the text using a pre-trained tokenizer to capture semantic representations accurately. The model we presented, Transformer-XL-bidirectional gated recurrent units (BiGRU)-bidirectional long short-term memory (BiLSTM), which is called Fusion Transformer-XL, surpassed the performance of the baseline models, attaining an accuracy of 98.17% and an F1-score of 98.18%. Local interpretable model-agnostic explanation (LIME) text explanations were used to understand the reasoning behind the model's choices and performed the cross-dataset evaluation of the model using the English dataset. This helped improve the clarity and reliability of the proposed method. Furthermore, implementing k-fold cross-validation ensures our model's robustness and adaptability across diverse data categories. The results of our study demonstrate the effectiveness of combining Transformer-XL with bi-directional recurrent networks for detecting cyberbullying in Bengali. This emphasizes the significance of using hybrid architectures to address intricate natural language processing problems in languages with limited resources. This study enhances the development of methods for detecting cyberbullying and opens up opportunities for additional investigation into language diversity and social media analytics.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2940"},"PeriodicalIF":3.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Tencent Meeting forensics based on memory reverse analysis.","authors":"Shilong Yu, Binglong Li, Lin Zhu, Heyu Zhang, Sen Yang, Zhangxiao Li, Wenzheng Feng","doi":"10.7717/peerj-cs.2963","DOIUrl":"10.7717/peerj-cs.2963","url":null,"abstract":"<p><p>Tencent Meeting, an instant meeting software, is widely used at present, but no research has been conducted on its forensics. Since the real-time data generated by such software during meetings will not be stored in the computer disk, the traditional disk forensics method against such software is no longer applicable and needs to obtain evidence through memory analysis. To extract meeting data transmitted during meetings, this article proposes a method for Tencent Meeting forensics based on memory reverse analysis. First, by analyzing the process storage and metadata format of Tencent Meeting in memory, an inverse metadata extraction algorithm is designed. Then, by analyzing the data structure of Tencent Meeting in memory, a meeting data stream engraving algorithm is developed. Finally, the experimental results indicate that the proposed method can effectively extract metadata information such as meeting time, meeting number, topic, and data flow information such as participants, message records, as well as transmitted files from the memory of Tencent Meeting, providing crucial digital evidence for digital crime investigation. Compared with other forensic analysis methods for instant meeting software, our proposed forensic method for Tencent Meeting conducts memory reverse analysis with the entire memory file, enabling the extraction of more comprehensive and abundant forensic data.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2963"},"PeriodicalIF":3.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 novel dilated weighted recurrent neural network (RNN)-based smart contract for secure sharing of big data in Ethereum blockchain using hybrid encryption schemes.","authors":"Swetha S, Joe Prathap P M","doi":"10.7717/peerj-cs.2930","DOIUrl":"10.7717/peerj-cs.2930","url":null,"abstract":"<p><strong>Background: </strong>With the enhanced data amount being created, it is significant to various organizations and their processing, and managing big data becomes a significant challenge for the managers of the data. The development of inexpensive and new computing systems and cloud computing sectors gave qualified industries to gather and retrieve the data very precisely however securely delivering data across the network with fewer overheads is a demanding work. In the decentralized framework, the big data sharing puts a burden on the internal nodes among the receiver and sender and also creates the congestion in network. The internal nodes that exist to redirect information may have inadequate buffer ability to momentarily take the information and again deliver it to the upcoming nodes that may create the occasional fault in the transmission of data and defeat frequently. Hence, the next node selection to deliver the data is tiresome work, thereby resulting in an enhancement in the total receiving period to allocate the information.</p><p><strong>Methods: </strong>Blockchain is the primary distributed device with its own approach to trust. It constructs a reliable framework for decentralized control <i>via</i> multi-node data repetition. Blockchain is involved in offering a transparency to the application of transmission. A simultaneous multi-threading framework confirms quick data channeling to various network receivers in a very short time. Therefore, an advanced method to securely store and transfer the big data in a timely manner is developed in this work. A deep learning-based smart contract is initially designed. The dilated weighted recurrent neural network (DW-RNN) is used to design the smart contract for the Ethereum blockchain. With the aid of the DW-RNN model, the authentication of the user is verified before accessing the data in the Ethereum blockchain. If the authentication of the user is verified, then the smart contracts are assigned to the authorized user. The model uses elliptic Curve ElGamal cryptography (EC-EC), which is a combination of elliptic curve cryptography (ECC) and ElGamal encryption for better security, to make sure that big data transfers on the Ethereum blockchain are safe. The modified Al-Biruni earth radius search optimization (MBERSO) algorithm is used to make the best keys for this EC-EC encryption scheme. This algorithm manages keys efficiently and securely, which improves data security during blockchain operations.</p><p><strong>Results: </strong>The processes of encryption facilitate the secure transmission of big data over the Ethereum blockchain. Experimental analysis is carried out to prove the efficacy and security offered by the suggested model in transferring big data over blockchain <i>via</i> smart contracts.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2930"},"PeriodicalIF":3.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Leveraging large language models for spelling correction in Turkish.","authors":"Ceren Guzel Turhan","doi":"10.7717/peerj-cs.2889","DOIUrl":"10.7717/peerj-cs.2889","url":null,"abstract":"<p><p>The field of natural language processing (NLP) has rapidly progressed, particularly with the rise of large language models (LLMs), which enhance our understanding of the intrinsic structures of languages in a cross-linguistic manner for complex NLP tasks. However, commonly encountered misspellings in human-written texts adversely affect language understanding for LLMs for various NLP tasks as well as misspelling applications such as auto-proofreading and chatbots. Therefore, this study focuses on the task of spelling correction in the agglutinative language Turkish, where its nature makes spell correction significantly more challenging. To address this, the research introduces a novel dataset, referred to as NoisyWikiTr, to explore encoder-only models based on bidirectional encoder representations from transformers (BERT) and existing auto-correction tools. For the first time in this study, as far as is known, encoder-only models based on BERT are presented as subword prediction models, and encoder-decoder models based on text-cleaning (Text-to-Text Transfer Transformer) architecture are fine-tuned for this task in Turkish. A comprehensive comparison of these models highlights the advantages of context-based approaches over traditional, context-free auto-correction tools. The findings also reveal that among LLMs, a language-specific sequence-to-sequence model outperforms both cross-lingual sequence-to-sequence models and encoder-only models in handling realistic misspellings.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2889"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"The geometry of meaning: evaluating sentence embeddings from diverse transformer-based models for natural language inference.","authors":"Mohammed Alsuhaibani","doi":"10.7717/peerj-cs.2957","DOIUrl":"10.7717/peerj-cs.2957","url":null,"abstract":"<p><p>Natural language inference (NLI) is a fundamental task in natural language processing that focuses on determining the relationship between pairs of sentences. In this article, we present a simple and straightforward approach to evaluate the effectiveness of various transformer-based models such as bidirectional encoder representations from transformers (BERT), Generative Pre-trained Transformer (GPT), robustly optimized BERT approach (RoBERTa), and XLNet in generating sentence embeddings for NLI. We conduct comprehensive experiments with different pooling techniques and evaluate the embeddings using different norms across multiple layers of each model. Our results demonstrate that the choice of pooling strategy, norm, and model layer significantly impacts the performance of NLI, with the best results achieved using max pooling and the L2 norm across specific model layers. On the Stanford Natural Language Inference (SNLI) dataset, the model reached 90% accuracy and 86% F1-score, while on the MedNLI dataset, the highest F1-score recorded was 84%. This article provides insights into how different models and evaluation strategies can be effectively combined to improve the understanding and classification of sentence relationships in NLI tasks.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2957"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil.","authors":"Carlos André de Mattos Teixeira, Thabatta Moreira Alves de Araujo, Evelin Cardoso, Marcos Antonio Costantin Filho, João Weyl Costa, Carlos Renato Lisboa Frances","doi":"10.7717/peerj-cs.2917","DOIUrl":"10.7717/peerj-cs.2917","url":null,"abstract":"<p><p>Visual inspection is essential to ensure the stability of earth-rock dams. Periodic visual assessment of this type of structure through vegetation cover analysis is an effective monitoring method. Recently, multispectral remote sensing data and machine learning techniques have been applied to develop methodologies that enable automatic vegetation analysis and anomaly detection based on computer vision. As a first step toward this automation, this study introduces a methodology for land cover segmentation of earth-rock embankment dam structures within the Belo Monte Hydroelectric Complex, located in the state of Pará, northern Brazil. Random forest (RF) ensemble models were trained on manually annotated data captured by a multispectral sensor embedded in an uncrewed aerial vehicle (UAV). The main objectives of this study are to assess the classification performance of the algorithm in segmenting earth-rock dams and the contribution of non-visible band reflectance data to the overall model performance. A comprehensive feature engineering and ranking approach is presented to select the most descriptive features that represent the four dataset classes. Model performance was assessed using classical performance metrics derived from the confusion matrix, such as accuracy, Kappa coefficient, precision, recall, F1-score, and intersection over union (IoU). The final RF model achieved 90.9% mean IoU for binary segmentation and 91.1% mean IoU for multiclass segmentation. Post-processing techniques were applied to refine the predicted masks, enhancing the mean IoU to 93.2% and 91.9%, respectively. The flexible methodology presented in this work can be applied to different scenarios when treated as a framework for pixel-wise land cover classification, serving as a crucial step toward automating visual inspection processes. The implementation of automated monitoring solutions improves the visual inspection process and mitigates the catastrophic consequences resulting from dam failures.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2917"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"GAPNet: Single and multiplant leaf disease classification method based on simplified SqueezeNet for grape, apple and potato plants.","authors":"Özge Nur Özaras, Asuman Günay Yılmaz","doi":"10.7717/peerj-cs.2941","DOIUrl":"10.7717/peerj-cs.2941","url":null,"abstract":"<p><p>Humans need food to sustain their lives. Therefore, agriculture is one of the most important issues in nations. Agriculture also plays a major role in the economic development of countries by increasing economic income. Early diagnosis of plant diseases is crucial for agricultural productivity and continuity. Early disease detection directly impacts the quality and quantity of crops. For this reason, many studies have been carried out on plant leaf disease classification. In this study, a simple and effective leaf disease classification method was developed. Disease classification was performed using seven state-of-the-art pretrained convolutional neural network architectures: VGG16, ResNet50, SqueezeNet, Xception, ShuffleNet, DenseNet121 and MobileNetV2. A simplified SqueezeNet model, GAPNet, was subsequently proposed for grape, apple and potato leaf disease classification. GAPNet was designed to be a lightweight and fast model with 337.872 parameters. To address the data imbalance between classes, oversampling was carried out using the synthetic minority oversampling technique. The proposed model achieves accuracy rates of 99.72%, 99.53%, and 99.83% for grape, apple and potato leaf disease classification, respectively. A success rate of 99.64% was achieved in multiplant leaf disease classification when the grape, apple and potato datasets were combined. Compared with the state-of-the-art methods, the lightweight GAPNet model produces promising results for various plant species.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2941"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 malware detection method with function parameters encoding and function dependency modeling.","authors":"Ronghao Hou, Dongjie Liu, Xiaobo Jin, Jian Weng, Guanggang Geng","doi":"10.7717/peerj-cs.2946","DOIUrl":"10.7717/peerj-cs.2946","url":null,"abstract":"<p><p>As computers are widely used in people's work and daily lives, malware has become an increasing threat to network security. Although researchers have introduced traditional machine learning and deep learning methods to conduct extensive research on functions in malware detection, these methods have largely ignored the analysis of function parameters and functional dependencies. To address these limitations, we propose a new malware detection method. Specifically, we first design a parameter encoder to convert various types of function parameters into feature vectors, and then discretize various parameter features through clustering methods to enhance the representation of API encoding. Additionally, we design a deep neural network to capture functional dependencies, enabling the generation of robust semantic representations of function sequences. Experiments on a large-scale malware detection dataset demonstrate that our method outperforms other techniques, achieving 98.62% accuracy and a 98.40% F1-score. Furthermore, the results of ablation experiments show the important role of function parameters and functional dependencies in malware detection.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2946"},"PeriodicalIF":3.5,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"RiceChain-Plus: an enhanced framework for blockchain-based rice supply chain systems-ensuring security, privacy, and efficiency.","authors":"Bello Musa Yakubu, Abdullah Abdulrahman Alabdulatif, Pattarasinee Bhattarakosol","doi":"10.7717/peerj-cs.2926","DOIUrl":"10.7717/peerj-cs.2926","url":null,"abstract":"<p><p>The rice supply chain is a complex system that demands effective management to ensure reliability and efficiency, given the involvement of multiple stakeholders. Blockchain technology, with its decentralized and tamper-resistant nature, offers a promising solution for improving transparency, traceability, and credibility in agricultural supply chains. However, existing blockchain systems face several technological challenges, including security vulnerabilities, privacy concerns, and performance limitations. To address these issues, this article presents RiceChain-Plus, an enhanced architecture that incorporates a private Ethereum blockchain, proof of authority (PoA) consensus mechanism, mutual authentication, zero-knowledge proofs (ZKPs), a hybrid role-based access control (RBAC) and attribute-based access control (ABAC) system, and one-way hash functions. This approach enhances the rice supply chain's security, privacy, and efficiency by safeguarding sensitive data and ensuring confidentiality. Performance assessments show that RiceChain-Plus surpasses existing benchmark models, achieving the lowest average execution costs (44,634 gas), reduced energy consumption (9.38828E-05 J), higher throughput (0.071201 transactions/s), faster execution (44.5 ms), and quicker transaction times (14.045 s), while also improving scalability. A comprehensive security analysis further confirms the framework's resilience against various cyberattacks. These results highlight RiceChain-Plus as a secure, efficient, and effective solution for optimizing rice supply chain operations.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2926"},"PeriodicalIF":3.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}