Information resources estimation for accurate distribution-based concept drift detection

Machine learning applications must continually utilize label information from the data stream to detect concept drift and adapt to the dynamic behavior. Due to the computational expensiveness of label information, it is impractical to assume that the data stream is fully labeled. Therefore, much research focusing on semi-supervised concept drift detection has been proposed. Despite the large research effort in the literature, there is a lack of analysis on the information resources required with the achievable concept drift detection accuracy. Hence, this paper aims to answer the unexplored research question of “How many labeled samples are required to detect concept drift accurately?” by proposing an analytical framework to analyze and estimate the information resources required to detect concept drift accurately. Specifically, this paper disintegrates the distribution-based concept drift detection task into a learning task and a dissimilarity measurement task for independent analyses. The analyses results are then correlated to estimate the required number of labels within a set of data samples to detect concept drift accurately. The proximity of the information resources estimation is evaluated empirically, where the results suggest that the estimation is accurate with high amount of information resources provided. Additionally, estimation results of a state-of-the-art method and a benchmark data set are reported to show the applicability of the estimation by proposed analytical framework within benchmarked environments. In general, the estimation from the proposed analytical framework can serve as guidance in designing systems with limited information resources. This paper also hopes to assist in identifying research gaps and inspiring new research ideas regarding the analysis of the amount of information resources required for accurate concept drift detection.     

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.     

基于工作过程的《乳与乳制品检测技术》课程教学改革与实践

本文从传统教学模式中存在的问题、教学方法与手段的改革和考核方式的改革三个方面对包头轻工职业技术学院乳品工程学院《乳与乳制品检测技术》课程的教学改革进行了综述。     

工程项目管理沙盘模拟教学改革与实践

沙盘模拟是新型的体验式教学模式,本文针对工程项目管理课程,介绍了沙盘模拟教学模式的特点,对工程项目管理沙盘模拟的教学组织和教学内容进行了系统设计,结合教学实践提出了教学改革的建议。     

三本院校《光纤通信》课程教学的新探索

本文从三本院校光纤通信课程的性质和特点入手,对三本院校光纤通信课程教学存在的问题进行了分析。结合三本院校及学生的特点,提出一些相关的教学改革措施。     

《食品工艺学》教学改革初探

食品工艺学是食品科学与工程专业的主干课程,为提高教学质量,本文结合食品工艺学的教学内容、教学方法、实践教学和考核方式,介绍了教学过程中的一些改革措施。     

高校设计艺术专业实践教学探索与改革

实践教学在设计艺术类应用型人才的培养中,起着至关重要的作用。针对当前实践教学存在着校内、校外实践条件不足、教师重理论、轻实践的传统观念严重、实践教师力量的不足等众多问题,提出了实践教学模式探索与改革,包括进行人才培养方案的改革、加大校内实践环境的建设力度、加强校外实践基地的建设,力求毕业设计真题实做、实行双导师制、建立导师工作室以及积极组织学生参加大学生创新创业实践活动和学科竞赛等。实践证明,这种实践教学模式的探索与改革取得了一定的成效,增强了学生的实践动手能力。     

服饰图案创新设计教学思考

根据服饰图案课程教学的现状,针对目前教学中存在的问题,提出服饰图案课程教学改革的总体思路。运用新的教学方式方法,培养学生收集整理信息资源的能力,通过教学手段的多元化,改变学生的常规学习模式,打破传统的教学模式,积极引导学生的创新思维,进行主题式及实验式教学,使学生在进行深入创新设计的同时提高实践动手能力,为服饰图案教学的改革提供借鉴。     

材料力学教学几点体会

材料力学是工科专业开设的一门专业技术基础课。根据课程的特点及存在问题,提出了对材料力学教学的几点体会。     

Visual FoxPro数据库课程教学模式研究与实践

本文依据“Visual FoxPro数据库”课程体系的教学目标：面向应用、提高能力、培养创新,探讨在该课程的教学过程中突出应用能力和创新能力培养的新的教学模式,并取得了很好的效果。