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关键词： 计算机科学   国际顶级会议   优质稿源   国际影响力  

摘要： 【目的】针对计算机科学领域会强刊弱、期刊优质稿源不足的现状,探索本领域学术期刊借鉴会议成功经验的发展之道。【方法】结合Science China Information Sciences的实践,梳理顶级会议专题的组织流程,包括特约编辑的甄选、投稿主题和征稿时间的选择、编辑在组织和出版过程中的重要工作,以及多渠道的宣传推广措施,并分析专题产生的影响。【结果】通过合理的组织和宣传,顶级会议专题获得了广泛的关注和较高的被引频次,并激发了作者向期刊相关学科投稿的热情。【结论】借鉴会议成功经验弥补期刊在评审、出版和交流方面的不足,从顶级会议争取优质稿源是计算机科学领域期刊提升学术水平和国际影响力的有效途径。

关键词： Computer science   Visualization   Limiting   Engineering profession   Government   Mathematics   Problem-solving  

摘要： Visualization, interactive computer graphics, and related topics have been a particularly dynamic area of computer science, producing advances that impact society. Working at times in a research laboratory and at times for two science funding agencies, I held positions at the level of an individual researcher, Director of a VR Laboratory, and funding agency Program Director. This article will discuss some of my experiences, including insight into how science programs in funding agencies are initiated.

关键词： 葡萄   组织培养   原生质体   瞬时表达  

摘要： 本研究以‘夏黑’葡萄(Vitis vinifera)为实验材料,通过调节不同植物生长调节剂浓度配比,探究‘夏黑’葡萄愈伤组织诱导及生根诱导的最适培养基,并在最适培养基条件下,以无菌苗叶片和绿色、黄绿色愈伤组织为材料,对原生质体提取及转化条件进行优化。结果表明,最适合茎段诱导芽的培养基为MS+1.0mg·L^(-1)6-苄氨基腺嘌呤(6-BA)+0.02 mg·L^(-1)α-萘乙酸(NAA),最适合茎段愈伤诱导的培养基为MS+1.0mg·L^(-1)6-BA+0.1 mg·L^(-1)NAA,最适合叶柄愈伤诱导的培养基为MS+2.0 mg·L^(-1)6-BA+0.1 mg·L^(-1)NAA,最适合叶片诱导的培养基为MS+2.0 mg·L^(-1)噻苯隆(TDZ)+1.0 mg·L^(-1)2,4-二氯苯氧乙酸(2,4-D)+0.2 mg·L^(-1)吲哚丁酸(IBA),最适合夏黑葡萄生根诱导的培养基为1/2MS+0.5 mg·L^(-1)IBA。以葡萄无菌苗叶片为最佳原生质体提取材料,在纤维素浓度为30 g·L^(-1)、离析酶浓度为5 g·L^(-1)、果胶酶浓度为4 g·L^(-1)、甘露醇浓度为0.6mol·L^(-1)时,28°C黑暗条件下酶解6 h,抽真空处理50 min后分离的原生质体最为理想。将能表达绿色荧光蛋白(GFP)的重组质粒pCAMBIA1302利用聚乙二醇4000(PEG4000)介导法转化原生质体后,观察到强烈的绿色荧光信号,表明分离的葡萄原生质体可以用作基因瞬时表达体系;并且当PEG4000浓度为300 g·L^(-1)时,转化效果最佳。利用CRISPR-P工具在葡萄VvZEP基因的第一个外显子中设计了2个sgRNA,通过酶切、PCR重组构建了含有2个靶标位点的靶向于VvZEP基因的CRISPR/Cas9载体pGREBn-VvZEP,为后续基因编辑载体转入葡萄原生质体提供了基础。

关键词： Risk analysis  

摘要： This paper proposes a new method for single-worker severity level prediction from already collected site images and video clips. Onsite safety observers often assess workers' severity levels during construction activities. While risk analysis is key to improving long-term construction site safety, omnipresent monitoring is still time-consuming and costly to implement. The recent growth of visual data captured actively on construction sites has opened a new opportunity to increase the frequency of worker safety monitoring. This paper shows that a comprehensive vision-based assessment is the most informative to automatically infer worker severity level from images. Efficient computer vision models are presented to conduct this risk analysis. The method is validated on a challenging image dataset first of its kind. Specifically, the proposed method detects and evaluates the worker state from visual data, defined by (1) worker body posture, (2) the usage of personal protective equipment, (3) worker interactions with tools and materials, (4) the construction activity being performed, and (5) the presence of surrounding workplace hazards. To estimate the worker state, a multitasked recognition model is introduced that recognizes objects, activity, and keypoints from visual data simultaneously, taking 36.6% less time and 40.1% less memory while keeping comparably performances compared to a system running individual models for each subtask. Worker activity recognition is further improved with a spatio-temporal graph neural network model using recognized per-frame worker activity, detected bounding boxes of tools and materials, and estimated worker poses. Finally, severity levels are predicted by a trained classifier on a dataset of images of construction workers accompanied with ground truth severity level annotations. In the test dataset assembled from real-world projects, the severity level prediction model achieves 85.7% cross-validation accuracy in a bricklaying task an

关键词： Erbium   Measurement   Intelligent systems   Task analysis   Computer science   Neural networks   Labeling  

摘要： Recent work has shown that entity resolution (ER) can be effectively performed by gradual machine learning (GML). GML begins with some automatically labeled easy instances and, then, gradually labels more challenging instances by iterative factor graph inference without human intervention. In GML, shared features serve as the medium for knowledge conveyance between easy instances and more challenging ones. The existing GML solution supposes that features play independent roles in gradual inference. However, in real scenarios, this assumption may be untenable since features are usually correlated with each other. To address this limitation, this article proposes an attention-enhanced approach to improve the accuracy of gradual inference. We first propose a method of spectral feature representation to map correlated features to close points in the same vector space and, then, present a model of attention neural network to learn the decisive features, given arbitrary combinations of features for improved feature weighting. Finally, our extensive experiments on real benchmark data have validated the efficacy of the proposed approach.

关键词： 计算机文化基础   教学方法   教学改革   评价体系  

摘要： 针对洛阳师范学院非计算机专业学生,计算机基础教学课时少,学生基础参差不齐,专业种类繁多等特点,给出一些新的方法。设计有特点有代表性的例子,充分使用现有网络优势进行线上辅导,强调帮助文件和网络帮助的重要作用,并构建科学合理的评价指标体系。提高了计算机基础教学的质量,在有限的课时内满足了不同层次不同专业的学生需要。

关键词： Contracts   Resource management   Indium phosphide   III-V semiconductor materials   Base stations   Closed-form solutions   Computer science   Heterogeneous computational resources allocation   virtualized base station   cloud-RAN   cellular networks   contract theory   incentive mechanism  

摘要： In this work, we develop a contract theory framework to tackle the allocations of heterogeneous baseband processing units (BBUs) in cloud radio access network. We first model a monopoly market by viewing the BBUs as a kind of resource. The infrastructure provider (InP), as the monopolist, owns all the heterogeneous BBUs of different processing abilities and maintaining costs, and leases them to multiple mobile network operators (MNOs) to gain profit. At the same time, the MNOs intend to rent reasonable amount of BBUs to provide services to their mobile clients. Then we propose a contract theory framework, in which contract items are optimized to maximize the InP's utility, while maintain the welfare of the MNOs. We design the optimal contracts with complete and asymmetric information on the MNOs. Our contract design achieves the near optimum solution to heterogeneous computational resource allocation even under the information asymmetric case. Our derivations indicate that the optimal contracts with asymmetric information achieve a lower utility for the InP than the ones with complete information and the utility reduction is higher when the BBUs are heterogeneous rather than homogeneous. Numerical results demonstrate that, the InP having heterogeneous BBUs can achieve a higher utility relative to having homogeneous BBUs, which is more profitable and realistic for the InP. Moreover, we regard Stackelberg game theoretic approach as a comparison, and show that our method is more realistic.

关键词： 高职院校   计算机科学与技术   实践教学   项目  

摘要： 我国计算机技术的快速发展对计算机技术专业人才的综合素质和能力提出了更高的要求。为了适应市场需求,高职院校计算机科学与技术专业应该做出教学上的调整。加强对学生实践操作能力的培养,既符合高职教育人才培养的特征,又可提升学生的就业能力。文章首先阐述了高职计算机科学与技术专业实践教学现状,然后对实践教学体系的优化进行分析,为提升高职计算机专业技术人才培养质量提供参考。

关键词： 黑水虻幼虫   pH   粗蛋白   粗脂肪   死亡率  

摘要： 研究旨在探讨培养基起始pH对黑水虻(BSFL)幼虫生长及虫体粗蛋白、粗脂肪含量的影响。孵化4 d的黑水虻幼虫被随机分为9组,每组6个重复,每重复30只黑水虻。分别在起始pH为2.0、3.0、4.0、5.0、6.0、7.0、8.0、9.0、10.0的含水量为40%的培养基中饲养6 d。结果表明:培养基起始pH不影响黑水虻幼虫生长速度(P>0.05),但显著影响黑水虻存活率(P<0.05),以pH为2.0时黑水虻幼虫存活率最高;培养基起始pH不影响黑水虻幼虫水分含量(P>0.05);随着培养基起始pH的升高,黑水虻幼虫粗蛋白含量线性下降(P<0.05),粗脂肪含量线性升高(P<0.05)。综上所述,碱性培养基有利于黑水虻幼虫脂肪沉积,而酸性培养基有利于蛋白质沉积。

关键词： Neural networks   Computer science   Social networking (online)   Tools   Task analysis   Indexes   Data mining   Network embedding   graph neural networks   social network analysis   data mining  

摘要： Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research. Semi-supervised network embedding takes advantage of labeled data, and has shown promising performance. However, existing semi-supervised methods would get unappealing results in the completely-imbalanced label setting where some classes have no labeled nodes at all. To alleviate this, we propose two novel semi-supervised network embedding methods. The first one is a shallow method named RSDNE. Specifically, to benefit from the completely-imbalanced labels, RSDNE guarantees both intra-class similarity and inter-class dissimilarity in an approximate way. The other method is RECT which is a new class of graph neural networks. Different from RSDNE, to benefit from the completely-imbalanced labels, RECT explores the class-semantic knowledge. This enables RECT to handle networks with node features and multi-label setting. Experimental results on several real-world datasets demonstrate the superiority of the proposed methods.