高校图书馆工作人员素质提高策略初探

现代社会的发展，给高校图书馆带来巨大的压力与挑战。图书馆员的整体素质如何，将关系到图书馆事业的未来发展。高校图书馆应从提高其馆员整体学历水平、建立和完善岗位培训制度、使全体馆员牢固树立终身学习的观念三方面来提高工作人员的整体素质，推动图书事业的持续发展。     

关于双语课程《数字电子技术》学习状况的调查与思考

以问卷调查的形式,通过描述性统计和定量分析,对我校双语课程《数字电子技术》学习状况进行了调查和分析,在此基础上,对双语教学提出了一些建议.     

研究性学习中的心理学思想探析

研究性学习视学生为一个完整的人，注重学生个性的培养，重视学生潜能的开发以及整体素质的协调，这其中渗透着激发学生学习动机、使学生达到自我实现等心理学思想．该文对研究性学习的几个特点的心理学思想进行了分析。     

大学生学习焦虑心理研究

通过分析大学生学习焦虑心理的成因以及学习焦虑控制问题。论述了大学生学习焦虑心理与学习成绩的关系。     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

基于课程标准的教学——以常见阴、阳离子的检验为例

以常见的阴、阳离子的检验为例，研究了如何实施基于标准的教学。校本教材的开发为标准、教材、教学、评价的一致性提供保障；以学生应知的和能做的驱动课堂活动；根据达成标准应有怎样的质量表现，试卷编制先于教学设计。课堂上，“教”“学”双方都明确学习目标，教师提供多种策略来满足学生多样的学习需要，如提供工具，搭建脚手架，并以“微”研究性学习的方式展开教学，给学生提供了充分的进步空间。     

核心素养为本的单元教学设计与实施——以“探究水的组成”为例

以“探究水的组成”教学为例，通过课标、教材及学习者分析，从认识角度、探究水平、认识水平等3个维度整体规划“身边的化学物质”主题单元目标学习进阶，明确“探究水的组成”课时目标，通过温故建模、据模探究、探究推理等3个阶段的教学实施，建立具体物质的研究思路模型，运用模型研究陌生物质（氢气），初步形成定量研究物质组成的能力。     

Learning attention-based strategies to cooperate for multi-agent path finding

Multi-agent path finding (MAPF) is a challenging multi-agent systems problem where all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding obstacles. In MAPF, it is a challenge to effectively express the observation of agents, utilize historical information, and effectively communicate with neighbor agents. To tackle these issues, in this work, we proposed a well-designed model that utilizes the local states of nearby agents and outputs an optimal action for each agent to execute. We build the local observation encoder by using residual attention CNN to extract local observations and use the Transformer architecture to build an interaction layer to combine local observations of agents. With the purpose of overcoming the deficiency of success rate, we also designed a new evaluation index, namely extra time rate (ETR). The experimental results show that our model is superior to most previous models in terms of success rate and ETR. In addition, we also completed the ablation study on the model, and the effectiveness of each component of the model was proved.     

基于深度学习的红外与可见光图像融合综述

图像融合技术是指从不同的源图像中提取并融合互补的信息,生成一幅信息量更丰富、对后续高级视觉任务提供足够支持的图像.红外与可见光图像融合（Infrared and Visible Image Fusion,IVIF）是图像融合领域的一个重要分支.近年来,深度学习技术在视觉计算领域表现出了良好的性能,尤其是基于自编码器、卷积神经网络、生成对抗网络等几种基于深度学习的IVIF技术得到了蓬勃发展.为此,对基于深度学习的IVIF算法的方法、数据集和评估指标等进行了总结和阐述;通过大量的实验,进行定性和定量的结果分析,对比了各类基于深度学习IVIF算法的性能;最后,讨论了该领域未来发展的一些前景和研究方向.     

数据驱动下交通异常行为的双层识别模型

为实现对车辆异常行为的准确识别,提高车辆行驶的安全性,提出了一种基于支持向量机(support vector machine, SVM)、监督学习与长短期记忆(long short term memory, LSTM)深度学习的交通异常驾驶行为双层识别模型。首先,对车辆轨迹数据筛除和滤波,构建异常行为数据集;其次,从异常行为轨迹特征中提取出特定异常行为的特征标签,并人为标定在训练集中;再次,构建SVM模型对训练集进行粗识别,基于SVM的二分法原理,从测试集中筛选出异常行为;最后,通过LSTM时间序列模型构建具体种类的异常行为模型,并通过深度学习的方法,从异常行为数据中细分为蛇形驾驶、急速变向、侧滑、大半径转弯、快速U型转弯、急刹车等具体的异常驾驶行为。本次实验选用下一代仿真(next generation simulation, NGSIM)数据中US-101高速公路和peachtree城市道路的数据集的轨迹数据验证SVM和LSTM双层识别模型的性能,包括均方根误差、识别准确率等。结果表明,构建的双层识别模型在第一层有98%的识别准确率,第二层有超过80%的识别准确率,可以较为准确地识...