Influence of content and creator characteristics on sharing disaster-related information on social media

Identifying the key factors of the disaster-related information propagation process can provide decision support for disaster management. This study characterizes the effects of content types, location, and social capital of social media users on the virality of disaster-related information. We found through the Weibo dataset of the Yiliang earthquake that the virality of different types of information can vary on the basis of the social capital of users who post the information. This study fills the current research gaps by examining the individual and joint effects of the content and creator characteristics on the virality of disaster-related information.     

Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

The purpose of this study was to explore the value of extraction of tumor features in contrast-enhanced ultrasonography (CEUS) images based on the deep belief networks (DBN) for the diagnosis of cervical cancer patients and realize the intelligent evaluation on effects of diagnosis and chemotherapy of the cervical cancer. An automatic extraction algorithm with the time-intensity curve (TIC) was proposed based on Sparse nonnegative matrix factorization (SNMF) in this study, and was applied to the framework of automatic analysis of cervical cancer tumors based on the deep belief networks, to assist doctors in the analysis of cervical cancer tumors. The framework was applied to the real clinical diagnostic data, and the feasibility of the method was verified by comparing the accuracy, sensitivity, and specificity. Later, the parameters of patients’ time to peak (TP), peak intensity (PI), mean transit time (MTT), and area under the curve (AUC) were obtained by drawing TICs, and the changes of p53 protein and ki-67 protein obtained by pathological section staining were analyzed to evaluate the therapeutic effect in the patients. It was found that the proposed model of tumor feature extraction based on the DBN had the higher accuracy (86.36%), sensitivity (83.33%), and specificity (87.50%). The related parameters of TIC curve obtained based on SNMF showed that there was a significant difference in p53 content between tissues with different degrees of disease (p?<?0.05), the PI of poorly differentiated tissues was significantly higher than that of those with high to medium differentiation (p?<?0.05). In addition, PI and AUC of patients after chemotherapy were significantly lower than that before chemotherapy (p?<?0.05), while MTT was significantly higher than that before chemotherapy (p?<?0.05). Therefore, the proposed TIC feature extraction of CEUS images based on SNMF and the automatic tumor classification based on deep learning can be used in the diagnosis and efficacy evaluation of cervical cancer patients.

     

Compiler-directed scratchpad memory data transfer optimization for multithreaded applications on a heterogeneous many-core architecture

The Journal of Supercomputing - The heterogeneous many-core architecture plays an important role in the fields of high-performance computing and scientific computing. It uses accelerator cores with...     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

Incorporating Actor-Critic in Monte Carlo tree search for symbolic regression

Neural Computing and Applications - Most traditional genetic programming methods that handle symbolic regression are random algorithms without memory and direction. They repeatedly search for the...     

深度学习的图像实例分割方法综述

实例分割是一项具有挑战性的任务,需要同时进行实例级和像素级的预测,在自动驾驶、视频分析、场景理解等方面应用广泛.近年来,基于深度学习的实例分割方法迅速发展,如两阶段检测器Faster R-CNN扩展出的聚焦于网络的精度而非速度的强大实例分割基准Mask R-CNN,一度成为实例分割的标杆.利用高速检测的单阶段检测器延伸出的实例分割算法YOLACT填补了实时实例分割模型的空白,具有较高的研究和应用价值.本文首先对实例分割算法进行了类别划分,然后对一些代表性的算法及其改进算法进行了深入分析,并阐述了相关算法的优缺点,最后对实例分割方法未来的发展进行了展望.     

基于上下文感知与自适应响应融合的相关滤波跟踪算法

针对当前相关滤波跟踪算法在抗背景干扰、响应融合方式以及模型更新策略上的不足,提出一种基于上下文感知与自适应响应融合的相关滤波跟踪算法.通过引入上下文感知技术,提高算法在背景杂波及遮挡等跟踪场景下的鲁棒性;通过研究HOG特征和颜色直方图特征二者响应图和响应值的特点,提出一种自适应响应融合方法,提升融合响应图的可靠性;在模型更新方面,采用了高置信度模型更新策略来减轻传统模型更新策略中模型污染及跟踪漂移的问题.实验结果表明,本文算法在OTB50数据集上达到了74.7%的跟踪精度,跟踪成功率为54.8%,均优于对比的主流相关滤波跟踪算法,并且在背景杂波、光照变化、遮挡、运动模糊等复杂跟踪场景中具有较好的跟踪精度与鲁棒性.     

高速网络流量管理系统研究

随着互联网用户的不断增加和用户需求的不断变化,互联网上的应用类型也在发生着日新月异的变化。各种类型的业务流量不断增长,网络的负担不断加重,网络管理的难度也随之增加。如何通过合适的流量管理技术,即网络应用协议的识别与带宽管理技术,来协调网络应用与安全、带宽增长与业务收益、网络扩容与用户体验之间的关系,是网络管理者与运营者亟待解决的重要挑战。文章的研究便着重于此,首先介绍了网络流量管理的基本概念与关键技术,随后对主流的开源流量管理系统和商用流量管理系统进行介绍。最后提出了一种协同式的分布流量管理系统,分布式的部署流量管理子系统通过分析控制中心协同检测与管理流量,可以有效的提高网络流量管理效率,提高未知网络协议的识别率,能够有效检测和防止DDos攻击。     

多字体印刷藏文内容监测系统研究

针对藏文舆情分析中藏文印刷品和藏文图片内容无法自动监测的难题,文章在深入分析印刷藏文字符特征和藏文文本特点的基础上,提出支持多字体印刷藏文内容监测系统的实现方法,重点阐述藏文字符的特征提取、分类算法以及藏文文本内容监测方法。     

微博影响力评价研究

微博作为一种被广泛使用的网络平台,其多样的特性满足了人们信息获取、人际交流等多方面的新需求,微博已成为人们日常生活的重要组成部分之一。微博影响力作为微博研究的一个热点,对于优化微博的信息整合、传输,对于深入了解微博发展方向、维护网络内容安全、促进网络发展具有非常重要的理论和实践意义。文章对当前主要的微博影响力评价方法进行了梳理和分析,并提出了针对中国微博的影响力评价模型。