IET Computers and Digital Techniques最新文献

{"title":"Evolutionary Game Analysis of Digital Product Copyright Protection in the Context of Generative Artificial Intelligence","authors":"Ying Gao,&nbsp;Tong Huang,&nbsp;Yang Jiang,&nbsp;Kexin Qu,&nbsp;Zisha Zhou,&nbsp;Fulei Shi","doi":"10.1049/cdt2/4161832","DOIUrl":"https://doi.org/10.1049/cdt2/4161832","url":null,"abstract":"<p>The rapid development of generative artificial intelligence (AI) poses new challenges to digital product copyright governance. Considering unauthorized training-data use, infringing AI-generated outputs, nonzero infringement costs, and uncertain enforcement outcomes, this paper constructs a two-population evolutionary game model between generative AI agents and digital copyright holders. The model incorporates infringement costs, probabilistic enforcement, and static and dynamic punishment mechanisms to examine the evolution of compliance, infringement, active enforcement, and passive tolerance strategies. The results show that under static punishment, the system may fall into widespread infringement, periodic oscillations, or passive tolerance. To overcome these limitations, a dynamic punishment mechanism is introduced, in which penalty intensity adjusts according to the AI side’s compliance rate. MATLAB simulations and sensitivity analyses based on normalized but industry-informed parameters verify the theoretical results. The findings indicate that dynamic punishment can improve system stability and guide periodic fluctuations toward a stable mixed–strategy equilibrium, thereby promoting a more predictable balance between AI compliance and copyright enforcement. This study provides theoretical support for adaptive copyright governance in the era of generative AI.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/4161832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994002","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":"Concurrency Frameworks for Embedded Systems: A Comparative Evaluation of FreeRTOS, RTIC, and Embassy","authors":"Guoping Wang","doi":"10.1049/cdt2/7881841","DOIUrl":"https://doi.org/10.1049/cdt2/7881841","url":null,"abstract":"<p>This paper presents a quantitative and qualitative comparison of three concurrency frameworks for embedded real-time systems: the C-based FreeRTOS kernel and the rust-based real-time interrupt-driven concurrency (RTIC) and Embassy frameworks. All three frameworks are implemented on an STM32F3 ARM Cortex-M4 platform using three representative applications: a single-task Blinky benchmark, a UART-to-LED event-signaling workload, and a Crane low profile vessel (LPV) subsystem integrating UART, CAN, and PWM tasks. Timing metrics, including task latency, periodic jitter, interrupt latency, and frame-to-register latency, are measured using the ARM data watchpoint and trace (DWT) cycle counter, while flash and RAM utilization are obtained through binary analysis. Across the Crane application, FreeRTOS achieves the lowest mean task-switch latency at 1110 cycles, compared with 2308 cycles for RTIC and 18,333 cycles for Embassy. RTIC provides the smallest memory footprint, using 17,224 bytes of flash and 1308 bytes of RAM, while Embassy and FreeRTOS use 20,784 and 20,444 bytes of flash and 5692 and 21,944 bytes of RAM, respectively. Embassy also shows highly repeatable timing behavior in selected application-level measurements, including a constant 442-cycle frame-to-register latency. Qualitatively, the study evaluates memory safety, concurrency reliability, predictability, scalability, and developer ergonomics. The results suggest that FreeRTOS is preferable for applications requiring minimal latency, RTIC is well suited for safety-oriented and RAM-constrained systems, and Embassy is attractive for scalable asynchronous I/O workloads.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/7881841","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753331","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":"Vision–LiDAR–Tactile Fusion Framework Optimized by Artificial Hummingbird Algorithm for Contact-Aware Embodied 3D Scene Perception","authors":"Mianqian Jia,&nbsp;Kunlong Bao","doi":"10.1049/cdt2/1592978","DOIUrl":"https://doi.org/10.1049/cdt2/1592978","url":null,"abstract":"<p>Embodied intelligent robots demand comprehensive 3D scene understanding via multimodal sensor fusion for complex physical environments, yet existing methods struggle with insufficient heterogeneous data complementarity and inflexible fusion weight optimization. This study presents Artificial Hummingbird Algorithm (AHA)-fusion, a vision–LiDAR–tactile fusion framework optimized by the AHA for contact-aware embodied 3D scene perception. The framework integrates a multimodal feature extraction pipeline using ResNet-50, PointNet++, and lightweight CNNs; an improved Artificial Hummingbird Algorithm (I-AHA) with chaotic traversal and elite learning to optimize fusion weights globally; and a contact-aware dynamic weight modulation mechanism for real-time weight adjustment based on tactile contact states. A multilevel temporal synchronization strategy achieves 3.2 ± 1.1 ms alignment accuracy (Acc). Experiments on nuScenes, EmbodiedScan, and a self-built tactile manipulation dataset show AHA-Fusion attains 70.2% mean average precision (mAP) and 73.8% nuScenes detection score (NDS) for 3D object detection and 67.4% mean intersection over union (mIoU) for semantic segmentation, outperforming state-of-the-art methods. Ablation studies confirm I-AHA surpasses learnable attention weights by 2.3 percentage points with enhanced stability, while the contact-aware mechanism boosts contact region detection by 5.2 percentage points. A small-scale real-robot pilot experiment further suggests that the proposed framework can support online contact-aware manipulation, although broader real-world validation remains necessary. These results demonstrate the potential of optimization-guided trimodal fusion for robust embodied 3D perception.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/1592978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752907","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":"Adaptive Hyperlink-Induced Topic Search-Based Deep Learning Model for Topic Refinement and Modeling Using BERT With Residual Generative Network","authors":"John Vaseekaran S.,&nbsp;Srinivasan N.","doi":"10.1049/cdt2/3725561","DOIUrl":"https://doi.org/10.1049/cdt2/3725561","url":null,"abstract":"<p>Due to the rapid growth of online and technology development, every people have an own mobile to search their doubts and easily find it out within a second. Hypertext induced topic search (HITS) is a link analyzed algorithm utilized to find reliable websites that satisfy broad-topic queries. To identify hub page that link to different authority pages and enhance better topic search recommendations, various conventional approaches have been proposed in recent times. Yet, these methods consume more computational duration and complexity ranges in the training and testing performance, enhancing poor performance. Moreover, it fails to manage with high-dimensional large quantity of input noisy data, resulting inaccurate outcomes. Therefore, a novel deep learning-based HITS model is developed to capture complex patterns in sparse, sequential and contextual data, thereby enabling personalized, dynamic topic search recommendations that customize the individual preferences in social media platforms. The applications of the adaptive HITS (AHITS) algorithm precisely focus on effective hyperlink selection. While analyzing the specific topics, the relevance of content in HITS algorithm is specifically improved using the random search-based sharpbelly fish optimization (RS-SBFO) strategy is designed. Then, the link contents are processed in the preprocessing phase. Here, the provided contents are preprocessed to eliminate noise and other related artifacts. Then, the acquired preprocessed contents are provided to the developed multihead attention-based bidirectional encoder representations from transformer with residual generative long short-term memory (MHA-BRGLSTM) model for topic modeling and refinement. Here, the developed MHA-BRGLSTM provides an effective decision regarding the specific topics and related analysis by utilizing deep context analysis. Thus, the acquired outcomes are topic modeled and refined. Experimental validations are conducted to compare the effectiveness of the developed framework.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/3725561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466899","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":"FPGA-Based Dual Path ConvNeXt-Tiny–ViT With Effective Feature Refinement for Skin Cancer Classification","authors":"Jamel Smida,&nbsp;Mohamed Karim Azizi,&nbsp;Anandh Sam Chandra Bose,&nbsp;Amor Smida","doi":"10.1049/cdt2/9183110","DOIUrl":"10.1049/cdt2/9183110","url":null,"abstract":"<p>Skin cancer is the dangerous life-threatening disease and is increasing rapidly due to environmental changes and lifestyle factors. The treatment for skin cancer is possible if detected at an early stage, but if not detected early, the possibility of successful treatment is reduced. Dermatologists use visual examinations to detect the type of skin cancer. However, this method is time-consuming, requires high skill, and has limited accuracy, especially at early stages when melanoma and benign lesions look similar and are difficult to differentiate. In recent years, deep learning (DL) has gained popularity in image processing by producing excellent results. This research proposes a novel hybrid DL model, dual path ConvNeXt-Tiny–vision transformer (ViT) with effective feature refinement. ConvNeXt-Tiny extracts spatial and local features, while ViT extracts global contextual features. Feature refinement fuses all the features and selects the most important ones for classification. Two public datasets, ISIC-2019 and HAM-10000, are employed to evaluate the proposed network. The collected dataset is affected by class imbalance. To address this issue, a novel variational autoencoder generative adversarial network (VAE–GAN) network is introduced, which improves the quality of generated images and enhances classification accuracy. The proposed network is compared with other DL models, including AlexNet, DenseNet, and Transformer. The results show that the proposed network outperforms other DL models, achieving 99.38% accuracy on ISIC-2019 and 99% on HAM-10000. To assess the significance of the proposed system, an ablation study is conducted. The experimental outcomes of the ablation study highlight the effectiveness of the proposed network. Additionally, comparisons with state-of-the-art (SOTA) methods are performed. After extensive evaluation, the proposed network is finalized and deployed on a field-programmable gate array (FPGA) to serve as a low-power, high-speed edge device. A hardware accelerator is designed with high processing efficiency, optimized data transfer, dual cache, and efficient scheduling. The FPGA’s performance is evaluated using metrics such as power consumption, inference time, and throughput, and it is compared with GPU and CPU performance. The proposed accelerator classifies skin cancer 6.88× faster than a CPU and 2.29× faster than a GPU, while consuming only 2.8 W of power. The results demonstrate that the proposed FPGA-based DL network is suitable for real-time skin cancer classification, offering high speed, maximum accuracy, and low power consumption.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/9183110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381174","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":"Collaborative Vision: Face Recognition and Detection Using Ensemble Deep Learning in a Federated Learning Framework","authors":"Wisam Hazim Gwad,&nbsp;Shahab Wahhab Kareem,&nbsp;Omar Ahmed Abdulkader,&nbsp;Bandar Ali Alrami Al Ghadmi","doi":"10.1049/cdt2/5109789","DOIUrl":"https://doi.org/10.1049/cdt2/5109789","url":null,"abstract":"<p>The continuous demand for reliable and privacy-sensitive face recognition and detection has led to improvements in machine learning and federated learning (FL) frameworks. While the centralized framework of existing solutions is beneficial for data collection, it presents significant privacy concerns and challenges due to the diversity of data types. To overcome these challenges, this study presents a new, scientifically grounded hybrid approach that combines ensemble deep learning with FL architecture. The authors achieve this by utilizing various deep learning models, such as AlexNet and vision transformers (ViTs), in conjunction with residual networks (ResNets), enhancing the overall efficiency of data prediction and enhancing the versatility of non-independent and identically distributed (Non-IID) datasets. The last component, FL, is the need to store personal user data in centralized repositories. Major developments include the application of secure aggregation techniques and differential privacy to avoid leakage of data, minimizing overhead communication, and designing strategic learning for different clients’ data distribution. Real-world testing on a distributed dataset with 50,000 facial images shows that the proposed framework can achieve a recognition accuracy of up to 95.6%, which is better than the best single-model FL systems and other similar benchmark methods. Due to the framework’s ability to process large volumes of data from noisy and imbalanced data sets, surveillance, healthcare, and the internet of things are viable application areas. This work highlights the possibility of developing such cascaded solutions using ensemble deep learning embedded in a FL architecture.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/5109789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324820","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":"Multimodal Medical Image Fusion in Spatial-Transformed Domains","authors":"Yisheng Rong,&nbsp;Yangyang Xu,&nbsp;Wenfei Zhang,&nbsp;Yue Dong,&nbsp;Huatao Zhang","doi":"10.1049/cdt2/3385495","DOIUrl":"https://doi.org/10.1049/cdt2/3385495","url":null,"abstract":"<p>Fusion of multimodal medical images has become an essential reference for clinical diagnosis and a prominent topic in information fusion research. However, balancing the fusion performance with computational efficiency remains a challenging issue. In this paper, we propose an effective and efficient hybrid fusion method for multimodal medical images that integrates the merits of both spatial and transform domains, rather than relying on a single approach. First, a highly efficient multi-resolution tool, the framelet transform (FT), is employed to decompose medical images from different modalities into a series of low- and high-frequency sub-images. Next, an improved version of the structure tensor is designed to fuse the low-frequency sub-images, while a modified side-window filter (SWF) model is applied to fuse the high-frequency sub-images. Finally, the inverse FT is used to reconstruct the final fused image. Extensive simulation experiments on more than 120 pairs of multimodal medical images validate the performance of the proposed method, with results demonstrating superior visual quality and objective evaluation metrics compared to several recently published state-of-the-art approaches.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/3385495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281956","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":"RABNet: A Residual Attention-Based Network for Visibility Restoration in Hazy Images","authors":"Syeda Rabail Zahra,&nbsp;Farhan Hussain,&nbsp;Ali Hassan,&nbsp;Tehseen Mazhar,&nbsp;Amal Al-Rasheed,&nbsp;Muhammad Amir Khan,&nbsp;Wasim Ahmad,&nbsp;Habib Hamam","doi":"10.1049/cdt2/8366822","DOIUrl":"https://doi.org/10.1049/cdt2/8366822","url":null,"abstract":"<p>Image dehazing enhances visibility and image quality, enabling better decision-making in critical applications such as surveillance, remote sensing, and autonomous driving. However, single-image dehazing remains challenging due to the need for smoothness in homogeneous regions, accurate edge preservation, texture fidelity, and artifact-free reconstruction. In this paper, we propose RABNet, a novel deep learning framework for single-image dehazing. Traditional approaches often suffer from lossy reconstruction and poor performance under uneven haze distribution. RABNet addresses these limitations by integrating three key modules: the Hazy Feature Extraction Module (HFEM), Visibility Restoration Module (VRM), and Dehazed Image Reconstruction Module (DIRM). The HFEM enhances the network’s ability to perceive haze characteristics across varying lighting, contrast, and structural scales, providing a strong foundation for restoration. The VRM promotes feature diversity by employing multiple activation functions and an attention mechanism to refine visibility cues. Finally, the DIRM reconstructs the clean image by estimating the residual haze component and combining it with the input. By modeling haze-related features effectively, RABNet restores the underlying clean image while preserving color, texture, and structural details. Extensive experiments on both synthetic and real-world datasets, including FRIDA, O-HAZE, I-HAZE, and NH-HAZE, demonstrate the robustness and superior performance of RABNet, achieving state-of-the-art results in terms of PSNR and SSIM.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/8366822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237628","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Deep Learning Fusion Model for Insider Threat Detection in High Risk Infrastructure","authors":"Sardar Muhammad Ali,&nbsp;Hani Alshahrani,&nbsp;Abdul Razzaque,&nbsp;Mana Saleh Al Reshan,&nbsp;Muhammad Yousaf,&nbsp;Ali Alqazzaz,&nbsp;Qasim Ali Arain,&nbsp;Asadullah Shaikh","doi":"10.1049/cdt2/6332765","DOIUrl":"https://doi.org/10.1049/cdt2/6332765","url":null,"abstract":"<p>Insider threats are a widespread and severe issue of critical infrastructure and cyber-physical systems (CPSs), in which traditional security measures are frequently not sufficient. Despite these machine learning (ML) methods being utilized in detecting insider threats, most of the data mining techniques utilized are based either on misuse or anomaly methods, which limits their flexibility and resistance in dynamic operations. In order to overcome these shortcomings, this study presents a hybrid deep learning framework that is built on ensemble learning, and it can detect the presence of insider threat by integrating the spatial and temporal behavioral features. The architecture suggested is based on the convolutional neural networks (CNNs), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) models. The conventional layers can be extracted to obtain spatial features of input sequences that are based on logs, whereas recurrent layers are used to forecast temporal dynamics and changing user behavior. The heterogeneous models are combined via a soft-averaging ensemble approach, which boosts the generalizability and robustness. Recursive feature elimination (RFE) is used to reduce dimensionality to enhance interpretability using a random forest-based ranking of feature importance. Moreover, synthetic minority over-sampling technique (SMOTE) is applied to address the issue of the existing class imbalance, making the model more sensitive to infrequent insider threats. Evaluation of the cyber emergency response team (CERT) insider threat dataset using experimental methods confirms that the proposed ensemble model is much more effective than traditional baselines, with a detection accuracy of 90% and a ROC area under the curve (AUC) score of 0.96, a true positive rate (TPR) of 0.96 and a fairly low false positive rate (FPR) of 0.128. This article helps to fill the gap between approaches based on misuse and others on anomaly, and a scalable, real-time-capable detection solution, as well as provides a solid architecture of human-centric security in digitized and complex CPS environments.</p>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":"2026 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cdt2/6332765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095346","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}