“大思政”格局下研究生“课程思政”的探索与实践

全面推进“课程思政”建设是落实立德树人根本任务的战略举措,也是提高人才培养质量的重要途径。当前,我国高校在实施“课程思政”过程中,面临着思想政治教育资源缺乏、授课教师思政教育能力不强、实施效果缺乏科学有效的评价与监督等问题。针对这种情况,本文提出了具体的应对策略,即构建一体化的“课程思政”教学体系,形成专业的“课程思政”教学团队,建立完善的“课程思政”评价体系,并以《算法设计与分析》课程为例,详细阐述“大思政”格局下研究生“课程思政”的创新实践。

基于候客点规划的空闲出租车路线推荐算法

为空闲出租车司机推荐有效的闲逛路线在提高出租车司机工作效率、减少乘客等待时间以及缓解交通压力方面具有重要作用。现有的研究工作主要集中于为空闲司机推荐完整的驾驶路线,没有考虑到真实路网环境下某些路段的可等待因素,使得推荐的路线因载客概率较低、行驶距离较长而花费成本较高。提出一种基于候客点规划的路线推荐算法,对出租车轨迹数据进行处理,并设计路径匹配算法将每个轨迹点与真实路段一一匹配。通过统计每个路段历史接载信息,并利用一种改进的多层感知机建立可预测时序接载概率的模型,结合路段的可等待因素设计一种最小花费成本的路线推荐算法。在真实数据集上的实验结果表明,与MNP、InExperence、Random算法相比,所提算法花费成本、巡航时间以及巡航路程均明显减少。

Hybrid Two-Phase Task Allocation for Mobile Crowd Sensing

As a result of the popularity of mobile devices,Mobile Crowd Sensing (MCS) has attracted a lot of attention. Task allocation is a significant problem in MCS. Most previous studies mainly focused on stationary spatial tasks while neglecting the changes of tasks and workers. In this paper,the proposed hybrid two-phase task allocation algorithm considers heterogeneous tasks and diverse workers.For heterogeneous tasks,there are different start times and deadlines. In each round,the tasks are divided into urgent and non-urgent tasks. The diverse workers are classified into opportunistic and participatory workers.The former complete tasks on their way,so they only receive a fixed payment as employment compensation,while the latter commute a certain distance that a distance fee is paid to complete the tasks in each round as needed apart from basic employment compensation. The task allocation stage is divided into multiple rounds consisting of the opportunistic worker phase and the participatory worker phase. At the start of each round,the hiring of opportunistic workers is considered because they cost less to complete each task. The Poisson distribution is used to predict the location that the workers are going to visit,and greedily choose the ones with high utility. For participatory workers,the urgent tasks are clustered by employing hierarchical clustering after selecting the tasks from the uncompleted task set.After completing the above steps,the tasks are assigned to participatory workers by extending the Kuhn-Munkres (KM) algorithm.The rest of the uncompleted tasks are non-urgent tasks which are added to the task set for the next round.Experiments are conducted based on a real dataset,Brightkite,and three typical baseline methods are selected for comparison. Experimental results show that the proposed algorithm has better performance in terms of total cost as well as efficiency under the constraint that all tasks are completed.

“云计算技术”课程多元化教学的探究与实践

“云计算技术”属于计算机领域中一门综合性非常强的专业课程,其不仅包含很多的理论知识点,而且涉及大量实践环节。不仅如此,随着科技的发展,该课程的知识结构也处于持续更新中,不断有新概念、新技术被纳入“云计算技术”课程中。所以,传统的教学模式无法满足其综合性强、知识结构更新快、实践动手要求高的课程教学要求。鉴于此,文章结合该课程特点与以往的教学经验,从基本理论教学、实验教学、前沿技术扩展3个维度出发构建出多元化的教学模式,以提高学生课堂的学习效率,强化教学效果。

SDN网络中基于分布式Sketch的流基数估计方法

在软件定义网络(SDN)中,统计流基数信息对于流量工程、流量重路由和攻击检测等很多应用都具有重要作用。现有的研究工作主要分为在单交换机上部署测量结构和多交换机协同测量两类。然而,这两类方案都无法实现全网流量覆盖,并且多交换机协同测量通常采用每个交换机独立测量再合并的方式,容易导致重复计数。为了解决上述问题,提出了一种基于分布式Sketch的流基数估计方法。该方法充分利用SDN控制平面集中控制的优势,协同利用每条流的最长连续公共子路径上的交换机,以构建该流的逻辑层计数结构。同时,建立了每条流从逻辑层计数结构到物理交换机空间的映射,使参与计数的交换机可以根据自身状态和实际负载情况动态调整映射区间,以实现全网交换机之间的负载均衡。以vHLL算法为例,实现了一个分布式流基数估计方法的原型,并在四元Fat-Tree网络拓扑下使用真实网络流量数据集进行了实验评估。实验结果表明,所提方法能够有效实现全网每流基数估计,在准确性方面,其ARE和AAE值最多优于对比实验方法94.7%和93.8%;在负载均衡方面,该方法充分利用全网所有交换机,其归一化后的平均数据包负载为0.394,低于所有对比方法,表现良好。