Telling the whole story : a manually annotated Chinese dataset for the analysis of humor in jokes

Humor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or not, or the degree of funniness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chinese. We propose a novel annotation scheme to give scenarios of how humor arises in text. Specifically, our annotations of linguistic humor not only contain the degree of funniness, like previous work, but they also contain key words that trigger humor as well as character relationship, scene, and humor categories. We report reasonable agreement between annotators. We also conduct an analysis and exploration of the dataset. To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor. We also contribute with a scarce and valuable dataset, which we will release publicly. © 2019 Association for Computational Linguistic

Optimization towards Efficiency and Stateful of dispel4py

Scientific workflows bridge scientific challenges with computational resources. While dispel4py, a stream-based workflow system, offers mappings to parallel enactment engines like MPI or Multiprocessing, its optimization primarily focuses on dynamic process-to-task allocation for improved performance. An efficiency gap persists, particularly with the growing emphasis on conserving computing resources. Moreover, the existing dynamic optimization lacks support for stateful applications and grouping operations. To address these issues, our work introduces a novel hybrid approach for handling stateful operations and groupings within workflows, leveraging a new Redis mapping. We also propose an auto-scaling mechanism integrated into dispel4py's dynamic optimization. Our experiments showcase the effectiveness of auto-scaling optimization, achieving efficiency while upholding performance. In the best case, auto-scaling reduces dispel4py's runtime to 87% compared to the baseline, using only 76% of process resources. Importantly, our optimized stateful dispel4py demonstrates a remarkable speedup, utilizing just 32% of the runtime compared to the contender.Comment: 13 pages, 13 figure

Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma

Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma (ccRCC), non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment. A total of 126 345 computerized tomography (CT) images from four independent patient cohorts were included for analysis in this study. We propose a V Bottleneck multi-resolution and focus-organ network (VB-MrFo-Net) using a cascade framework for deep learning analysis. The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation, with a Dice score of 0.87. The nuclear-grade prediction model performed best in the logistic regression classifier, with area under curve values from 0.782 to 0.746. Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk, with a hazard ratio (HR) of 2.49 [95% confidence interval (CI): 1.13-5.45, P = 0.023] in the General cohort. Excellent performance had also been verified in the Cancer Genome Atlas cohort, the Clinical Proteomic Tumor Analysis Consortium cohort, and the Kidney Tumor Segmentation Challenge cohort, with HRs of 2.77 (95%CI: 1.58-4.84, P = 0.0019), 3.83 (95%CI: 1.22-11.96, P = 0.029), and 2.80 (95%CI: 1.05-7.47, P = 0.025), respectively. In conclusion, we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRCC. The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments, which could provide practical advice for deciding treatment options.</p

A successive transmission medium access scheme with dynamic contention window for VLC system with saturated traffic

The visible light communication (VLC) network is usually relatively small scale and can provide high-data-rate information transmission, where multiple users get access to the network according to the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism specified by IEEE 802.15.7 standard. In this paper, we propose a novel dynamic contention window with successive transmission (DCW-ST) scheme to improve the performance of this channel access mechanism and to achieve better network throughput without delay performance degradation. Specifically, we propose to adjust the contention window dynamically to adapt to the time-changing network size. Further, we derive the contention window size to achieve trade-off of throughput and delay, and the minimum contention window size required for the throughput enhancement. In addition, in order to further improve the delay performance, we present a successive transmission scheme that allows the nodes which have completed one transmission successfully to get the chance of transmitting information successively according to the network condition. Simulations are performed for the VLC system in saturated traffic and compared with the theoretical performances, which demonstrate that our proposed scheme outperforms the legacy CSMA/CA of IEEE 802.15.7

A successive transmission medium access scheme with dynamic contention window for VLC system with saturated traffic

The visible light communication (VLC) network is usually relatively small scale and can provide high-data-rate information transmission, where multiple users get access to the network according to the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism specified by IEEE 802.15.7 standard. In this paper, we propose a novel dynamic contention window with successive transmission (DCW-ST) scheme to improve the performance of this channel access mechanism and to achieve better network throughput without delay performance degradation. Specifically, we propose to adjust the contention window dynamically to adapt to the time-changing network size. Further, we derive the contention window size to achieve trade-off of throughput and delay, and the minimum contention window size required for the throughput enhancement. In addition, in order to further improve the delay performance, we present a successive transmission scheme that allows the nodes which have completed one transmission successfully to get the chance of transmitting information successively according to the network condition. Simulations are performed for the VLC system in saturated traffic and compared with the theoretical performances, which demonstrate that our proposed scheme outperforms the legacy CSMA/CA of IEEE 802.15.7